A Feature-Based Approach for Sentiment Quantiﬁcation Using Machine Learning

: Sentiment analysis has been one of the most active research areas in the past decade due to its vast applications. Sentiment quantiﬁcation, a new research problem in this ﬁeld, extends sentiment analysis from individual documents to an aggregated collection of documents. Sentiment analysis has been widely researched, but sentiment quantiﬁcation has drawn less attention despite offering a greater potential to enhance current business intelligence systems. In this research, to perform sentiment quantiﬁcation, a framework based on feature engineering is proposed to exploit diverse feature sets such as sentiment, content, and part of speech, as well as deep features including word2vec and GloVe. Different machine learning algorithms, including conventional, ensemble learners, and deep learning approaches, have been investigated on standard datasets of SemEval2016, SemEval2017, STS-Gold, and Sanders. The empirical-based results reveal the effectiveness of the proposed feature sets in the process of sentiment quantiﬁcation when applied to machine learning algorithms. The results also reveal that the ensemble-based algorithm AdaBoost outperforms other conventional machine learning algorithms using a combination of proposed feature sets. The deep learning algorithm RNN, on the other hand, shows optimal results using word embedding-based features. This research has the potential to help diverse applications of sentiment quantiﬁcation, including polling, trend analysis, automatic summarization, and rumor or fake news detection. for The computed results show that with GloVe performed best for and and RNN with performed best for STS-Gold and

Introduction
The social web has changed the way people communicate. The emergence of social media channels has resulted in the rapid creation of textual content. People create and post their content using social interaction platforms such as the web, discussion forums, Facebook, Twitter, etc. The rapid growth of content has sentiment information, which offers the potential for researchers to obtain people's opinion through social media about entities including business, academia, products, marketing, etc. To extract meaningful information from raw data, a famous field known as sentiment analysis is in trend [1,2]. Sentiment analysis is an active research area that classifies opinions in negative, positive, and neutral texts. It also finds the grade of polarity (high, moderate, and mild). Sentiment analysis is carried out on three levels: document level, sentence level, and phrase level. Document-level sentiment analysis is the most popular and is followed by numerous opinion mining techniques. Document-level sentiment analysis creates groups of documents and classifies the target documents into the required set of classes. For binary classification, the target documents are classified as positive or negative, while for tertiary classification the required classes include positive, negative, and neutral. Document-level sentiment analysis does not consider diverse factors for analysis. Apart from document-level analysis, sentence-level analysis considers each sentence and counts its single supposition. Sentence-level sentiment analysis is based on the subjectivity of sentences. Document-level and sentence-level sentiment analysis do not give a clear understanding of the polarity of the text. Sentiment analysis has various research areas, including subjectivity analysis, sentiment polarity detection [3], sentiment quantification, etc. [4].
Sentiment quantification deals with the estimation of class labels of individual content. For sentiment quantification, various methods that include Classify and Count, Adjusted Count, and Instance-based Quantification Trees [5] are commonly used in different studies. However, an analysis of previous classification algorithms shows that for quantification, these standard algorithms are not an optimal solution. In this regard, research suggests that quantification should be considered as a different approach compared to classification and this research problem should be addressed using diverse approaches [6] Hence, it opens new research opportunities to explore different approaches and develop new methods in this domain.
Consequently, it raises the need to devise sentiment quantification-based methods that deliver high accuracy. To address the issue of accuracy, this research contributes to the field of sentiment quantification as follows: • Novel feature sets are proposed such as pos, tweet, specific, content, and sentiment, with the ranking of features carried out using feature selection approaches. • Deep features including word2vec and GloVe are used for sentiment analysis, and these features are considered for sentiment quantification. The remainder of this paper is organized as follows: Section 2 provides a review of existing research studies in the relevant literature. Section 3 provides details of the proposed research methodology. Section 4 provides a comprehensive discussion of the empirical-based results. Section 5 concludes the paper.

Related Work
Here, we discuss the existing research work of quantification based on sentiment analysis, which further divides into three main classes: aggregated methods, non-aggregated methods, and ensemble-based methods.

Sentiment Quantification
Sentiment quantification is the process of detecting frequent data. It is also known as prevalence estimation [7]. Quantification is used in different fields to deal with aggregated data. Sentiment quantification has various research applications, with some of them discussed here. The sentiment quantification method is used to detect communities [8], quantification of cross-lingual language [9], public health monitoring [10], and for tweet classification [11].

Aggregated Methods
The quantification approach is preferred to predict the class prior probabilities. Classify and Count (CC) is a famous technique used for quantification. However, CC is lacking in the estimation of the class distribution. Newer approaches are presented to overcome the processing limitation of CC based on Sample Means Matching (SMM). SMM is very effective in quantifying a large amount of data per second. Twenty-five datasets are taken to perform the experiments. The proposed technique has outperformed the existing methods of quantification [12]. Further, a model titled Ordinal Quantification based on trees is proposed. The purpose of the proposed technique is to accurately count the frequency of each class of unlabeled items in text. In ordinal quantification, the order of the class is defined. The same approach is utilized to find the highest stars in products' reviews by analyzing their class prevalence over time. The proposed approach is evaluated on the SemEval2016 dataset and outperforms state-of-the-art methods [13].
Classifying data in deep layers is a complex task. Various techniques such as Neural and Statistical Machine Translation are used for this purpose, but are lacking in encoding and decoding while learning data from deep layers. To address these issues, Expectation Maximization (EM) is a quantification technique used for the automatic detection of errors in Arabic text to overcome the shortcomings of neural and statistical translation methods. EM dynamically combines information around layers. Moreover, during training, Kullback-Leibler divergence (KLD) is used to improve the model's performance. The proposed approach is evaluated on two standard datasets, namely QALB-2014 and QALB-2015. The experiments showed that the proposed approach outperformed the previous techniques in terms of F1 score [14].
EM is also applied in the field of rumor detection for Arabic tweets. The proposed method is based on a semi-supervised (EM) method to extract the user and contentbased features from tweets. Both feature sets are tested to check their significance. The proposed feature sets are trained through a semi-supervised (EM) method with a small base of labelled data. The proposed method was compared with the Gaussian method and outperformed the baseline with 78.6% accuracy [15].
An estimation of class proportions based on counting the classification errors is also used for classification purposes. A new method is proposed to adjust the classification errors by building confidence intervals. The model is introduced for the quantification of social media. The proposed approach is better than previous approaches that used the accurate estimation intervals [16].
The CC method has given rise to many other derived methods. "QuaNet", another derived method, is introduced using Recurrent Neural Network (RNN) to learn "quantification embeddings". These embeddings are firstly learned by a model then elaborated by CC. This approach is tested on Kindle, IMDb, and HP datasets. The results have shown the effectiveness of this model over existing quantification techniques [17].

Non-Aggregated Methods
González-Castro et al. [18] developed a model to quantify data based on a divergence measure. Hellinger distance is used for data distribution, validation, and to find the mismatch between test and validation. Prior probability estimation is used to minimize divergence. HDx and HDy are two types of Hellinger distance, where HDy needs output from the classifier and HDx does not need input from the classifier. Hopkins et al. introduced a non-parametric technique to quantify data [19]. The proposed approach quantifies data without any need of classification. American presidential blogs were selected as a dataset, with the proposed method decreasing the unbiased estimation. Software application was developed to quantify thousands of opinions about the US presidency.

Ensemble-Based Methods
Ensemble learners combine some weak learners. Some aggregated methods are combined to address the data distribution issues in sentiment analysis. Count HDy and Adjusted Classify are combined to make an ensemble model. CC, AC, PCC, PAC, and HDy are applied for learning the proposed ensemble model. Two schemes are presented to learn and predict. All learners are used to give prediction, then four sets of measures are applied to select the best model [20].
Ensemble methods give optimal results by building various training sets. Each model is then trained using data distribution techniques for quantification. The proposed methods categorize the errors of data distribution to enhance the performance of ensemble learners. The model explicitly addresses the binary quantification problem by focusing on the change in the expected distribution for each class. The results have shown that ensemble-based method have outperformed prior techniques [21].
The ensemble method is also explored in the field of soundscape ecology. A new approach is introduced which combines quantification and classification to train the CNN to classify classes of birds. The experiments show that the quantification performed better than the classification for the classification of bird species [22].
To obtain the optimal accuracy for sentiment quantification, machine learning techniques reported promising results. However, due to the sensitive nature of sentiments in the opinion-seeking process, there is a need to achieve more optimal results. Non-lexicon-based approaches are not widely applied for sentiment quantification. The role of diverse features can be exploited to find their role in improving the classification accuracy of sentiment quantification along with non-lexicon approaches. Some of these studies are summarized in Table 1.  [21] 2017 Data distribution and quantification.

Problem Statement and Formulation
Accuracy is an important parameter in the field of sentiment analysis. In the literature, various feature sets have been exploited using machine learning techniques to improve the results. However, there is still a need to investigate those feature sets for the emerging domain of sentiment quantification and to further improve accuracy because of the sensitive nature of sentiment in the opinion-seeking process. There is a need to contribute to the field of sentiment quantification to inquire about the impact of feature sets for sentiment quantification. In addition, as existing research studies only focus on machine learning, there is a need to explore deep learning approaches.
Formally, the research problem is to estimate the dispersion of a set D = d 1 , d 2 , . . . , d q A of unlabeled documents across a set C = c 1 , c 2 , . . . , c p S of classes. In our research, the relevant literature deals with |C| = 3. There are three classes: positive, neutral, and neg-ative. As our focus is the SLMC quantification task, we consider the measures that have been proposed for evaluation. Some of the notations include quantification loss denoted by ∇(p, p, D, C), error estimation denoted by ∇, and distribution denoted by p for set D and class C by another distributionp.

Proposed Research Methodology
This segment describes the approach used to quantify tweets based on sentiment analysis. A framework is proposed to give insights into the steps followed for sentiment quantification. A detail discussion follows on the feature engineering, algorithms applied, dataset considered, and performance evaluation measure used in this research.

Framework for Sentiment Quantification
The proposed model demonstrates the procedure carried out for sentiment quantification, as shown in Figure 1. In the first step, cleansing of the standard datasets (SemEval2016, SemEval2017, STS-Gold, and Sanders) is performed using data preprocessing techniques. Data preprocessing includes spaces removal, tokenization achievement, stop-words removal, case conversion, removal of words of less than three letters, and lemmatization for content feature extraction. In the second step, features based on content, POS (part of speech), tweet specific, and sentiment features are extracted using libraries of Python. Parameter settings for optimizing all classifiers are shown in Table 7. Further, the traditional machine learning approaches and deep learning approaches such as NB, AdaBoost, DT, RF, SVM and RNN, CNN_LSTM, and DBN are applied for sentiment quantification. Afterward, to count and classify the instance of data, the Classify and Count (CC) method is applied. Next, to evaluate the performance of machine learning classifiers, performance evaluation measures are applied for sentiment quantification. POS tagging is applied to understand the nature of content-based features. The verb feature is used to obtain the action of an entity. The adjective count is considered because adjectives show the negative and positive characteristics of a tweet. The content-based feature exploits the diverse characteristics of text in tweets. Question marks, exclamation marks, and special characters are counted to check whether a person is asking a question or trying to attract attention. The retweet feature is also important to check if a tweet con-

Feature Engineering
Feature engineering consists of feature extraction and selection to achieve optimal accuracy. The selection of features has a major impact in achieving the desired results. Here, the discussion is divided into subparts such as proposed features, baseline features, and deep features to elaborate on the features' impact on quantification accuracy. It also presents the selection and ranking of the features.

Proposed Feature Sets
To perform sentiment quantification, sentiment-based features are extracted through a sentiment-based lexicon, Vader. Vader is well known for the computation of sentiment features and is also used in various research studies [23,24].
POS tagging is applied to understand the nature of content-based features. The verb feature is used to obtain the action of an entity. The adjective count is considered because adjectives show the negative and positive characteristics of a tweet. The content-based feature exploits the diverse characteristics of text in tweets. Question marks, exclamation marks, and special characters are counted to check whether a person is asking a question or trying to attract attention. The retweet feature is also important to check if a tweet contains facts. If a tweet is frequently retweeted, it potentially relates to a sensitive topic containing more sentiments. The mention feature is considered to check if another person is added to the discussion. Moreover, the URL feature is used to obtain the number of URLs shared by users to support their point of view. The hashtag contains the content topic; therefore, this feature is also considered. A list of proposed features is shown in Table 2. Features such as n-gram, word2vec and Bag-of-Words (BoW) are also used for sentiment quantification. The n-gram is a technique of word embedding, while BoW is used in natural language processing (NLP). BoW takes frequencies of each word to train a classifier. Term frequency-inverse document frequency (TF-IDF) finds the frequency of a particular word in text. Word2vec and GloVe are used to represent words into vectors. The feature word2vec also finds the syntactic and semantics' similarity between words, while GloVe divides words into clusters to find similar and dissimilar words.

Feature Selection and Ranking
Optimal features increase the performance of classifiers. To find the optimal feature sets, three widely used feature selection methods are applied: Information Gain (IG), Gain Ration (GR), and Relief-f. IG is suitable for biased data and decreases due to its mutual information formula. GR computes the difference of attributes and considers the features with small difference. Relief-F computes the closest neighbors for all attributes.
Optimal features are selected through feature selection techniques, with those features having less importance and a negative influence on the target class omitted. Features ranked by feature selection algorithms according to their importance are shown in Table 3.  Table 3 shows that the sentiment features have a greater impact than other features. Negative sentiments and negative emoticons have a greater impact and are ranked higher than positive sentiments, with their importance consistent with existing research studies [25]. The adjective and adverbs features of POS have high scores and show the action of an object. Verbs have a greater impact than nouns due to their nature and show the attributes of any entity. POS features have a greater impact for predicting the target class.
Content features such as WH, quoted, and repetitive content-based features have high scores due to the subjective and opinionative nature of features, respectively [26]. Then, special characters followed by exclamation marks are ranked higher, showing the discussion within content. Another content-based feature, URL, has a high score as this focuses on the opinion of the subject. Hashtags are ranked low as they relate to topics that are important both in objective and opinionative content. Then follows hashtags that contain topics of the content which can be retweets and contain no sentiments.
Among the baseline features, TF-IDF and n-gram have higher scores than BoW. Among deep features, GloVe has a higher score than word2vec due to the fast processing of training. In addition, GloVe combines the benefits of the word2vec-based skip-gram model in word analogy tasks such as sentiment analysis and stance classification.

Classification Algorithms Applied
This subsection discusses the machine learning technique applied for sentiment quantification. Machine learning techniques are divided into three categories: traditional algorithms, ensemble learners, and deep learners.

Machine Learning Techniques
Some machine learning approaches applied on tweets for sentiment quantification are discussed.

Support Vector Machine (SVM)
SVM is based on linear regression. It uses high margins for high-dimensional data to classify negative and positive features. SVM optimization is calculated through the formula shown in Equations (1) and (2).
where n is the number of trainings, i is a linear combination of training inputs, q is the training output, m is the cost function, and x and y measure the similarity of the dot product of c. SVM is not suitable for noisy and large datasets due to more execution time being required for the training process.

Naïve Bayes (NB)
NB is a conditional probability-based classifier. The NB probability estimation formula is given in Equation (3), where X is an event, Z is the evidence, P(X) is the probability of an event before the evidence is seen, and P(X|Z) is the probability of an event after the evidence is seen.
NB gives better performance for categorical data than numerical data.

Decision Tree (DT)
DT is based on the rule of data decision and works on the principal of entropy and Information Gain (IG) techniques. DT helps to reduce the execution time of preprocessing for missing attributes. The entropy is calculated using the formula in Equation (4).
where P h is the probability that an attribute belongs to class m. To process the information in bits, the log 2 function is used. While En(X) is the required entropy for the class label, it is also known as Entropy.

Ensemble Learning Techniques AdaBoost
AdaBoost is an ensemble method that aggregates the strong and weak learners. This technique helps to give more accurate decisions for predicting the target class. This method is also favorable for attributes that are misclassified during prediction. While training, each element is assigned a weight. The weight assignment calculation is shown in Equation (5).
where e is the number of elements to be trained. Misclassified instances are computed as shown in Equation (6).
Random Forest (RF) RF is based on the technique of regression models. RF is suitable for high-variation data and calculate average to compute. RF works on a strategy of votes to calculate responses on data attributes. This approach uses a bagging method k times. For Q = 1... m, RF trains its regression tree by the formula given in Equation (7).

Deep Learning Techniques Deep Belief Networks (DBN)
DBN is a deep learning technique that follows the method of probability and statistics. DBN architecture contains hidden layers and blocks, with layers interconnected but blocks separated from each other. For sentiment analysis, DBN is in trend to exploit its efficiency for prediction [27].

CNN-LSTM
CNN (Convolutional Neural Network) is a deep learner but is not capable of calculating long-distance dependencies in data. LSTM (Long Short-Term Memory) can work well with long-distance dependencies and is combined with CNN to achieve the desired result for any biased datasets. CNN, along with LSTM, is applied for sentiment analysis and sequence-based text processing [28].

Recurrent Neural Network (RNN)
RNN is also a deep learner and is preferable for text processing and language translation. It works on the rule of memory. The previous output is saved and fed as input for the next phase. This strategy helps its sequential processing. RNN is applied for sentiment analysis [29].

Datasets
This subsection discusses the details of datasets selected for experimentation.

SemEval2017
SemEval2017 is a famous multilingual dataset. This dataset consists of tweets in two languages: Arabic and English. English tweets are higher in number than Arabic tweets, which are only 19% of the dataset. The dataset contains 6100 testing and 3355 training tweets in Arabic, with 12,284 testing and 50,333 training tweets are in English. The details of the dataset, which has been used in earlier studies [32,33], are shown in Table 4.

STS-Gold
STS-Gold may present different sentiment labels because tweets and targets (entities) are annotated individually [34]. This dataset contains 1.6 million manually classified tweets. There were 1.28 million tweets used for training and 3.2 million tweets used for testing purposes.

Sanders
The Sanders [35,36] [N2-N4] dataset is manually labelled by one annotator and consists of 5512 tweets. We have used 4410 tweets for training and 1102 tweets for testing purposes.

Performance Evaluation Measures
This subsection describes the performance evaluation measures used for sentiment quantification.

Absolute Error (AE)
This measure corresponds to the average absolute difference between the predicted class prevalence and the true class prevalence, using Equation (8).

Relative Absolute Error (RAE)
Relative absolute error (RAE) addresses the trouble that occurred in normalized absolute error by scaling the value p c j − p c j in Equation (9) with the true class prevalence.

Kullback-Leibler Divergence (KLD)
Another measure that has become the standard metric of quantification is normalized cross-entropy, better known as Kullback-Leibler divergence (KLD), which is used as a quantification measure and is defined in Equation (10).

Results and Discussion
According to the literature, there is room to improve the accuracy for sentiment-based quantification. Sentiment quantification is not addressed with feature-based approaches to achieve the desired accuracy. To address this problem, we have proposed various feature sets to reach the optimal accuracy for quantification of tweets based on sentiment analysis. To evaluate our feature-based framework, machine learning approaches which are subdivided into three levels, conventional algorithms, ensemble learners, and deep learning approaches, are applied on the SemEval2016, SemEval2017, STS-Gold, and Sanders datasets. To evaluate the performance of classifiers, performance evaluation metrics are applied.

Single Feature Sets
Detailed experiments are performed to evaluate the effectiveness of our proposed features for the sentiment quantification task. To achieve this aim, each proposed feature set is tested on both datasets to obtain detailed analysis. To evaluate their impact, NB, SVM, and DT conventional algorithms and AdaBoost and RF ensemble learners are applied on each set including POS, content, sentiment, and tweet specific. The results suggest that POS features show more effective results when applied with AdaBoost than when evaluated through performance evaluation metrics. AdaBoost dominated the other classifiers in terms of a lower error rate (KLD = 0.0213) for SemEval2016, SemEal2017 (KLD = 0.0214), STS-Gold (KLD = 0.0129), and Sanders (KLD = 0.0169), as shown in Table 5.

Combination of Feature Sets
To take the experiments to the next step, the proposed feature sets are combined with each other to determine the optimal pair of feature sets. The proposed features are combined in groups such as "sentiment + content" (SC), "sentiment + tweet specific" (ST), "sentiment + POS" (SP), "POS + tweet specific" (PT), "POS + content" (PC),"content + tweet specific" (CT), "sentiment + POS + content" (SPC), "sentiment + content + tweet specific" (SCT), "sentiment + POS + tweet specific" (SPT), "POS + content + tweet specific" (PCT), and all feature sets. The results have shown that when all proposed features are combined, they outperform all single feature sets. SVM outperformed other classifiers when applied with all feature sets "sentiment + POS + content + tweet specific" (SPCT). SVM has more promising results with a lower error rate for all four datasets, with KLD = 0.014 for SemEval2016, 0.013 for SemEval2017, 0.0051 for STS-Gold, and 0.0092 for Sanders, as shown in Tables 6-9, respectively.

Optimal Feature Sets
The results analysis is also represented in Figures 2 and 3. Results have shown the impact of POS as a single feature set. POS contains the action of an object and also important information. Thus, when applied with machine learning algorithms, it has shown promising results and a lower error rate for sentiment quantification, as shown in Figure 2 for all four datasets. When the feature sets are combined their effectiveness is increased, which shows the usefulness of these features that contain meaningful information and outperformed other approaches when applied with SVM for all four datasets, as shown in Figure 3.

Results of Deep Features
Some of the deep features are also exploited to find out their impact on sentiment quantification. Deep features, including GloVe, BoW, word2vec, and n-gram, are extracted from all four datasets, SemEval2016, SemEval2017, STS-Gold, and Sanders. The deep features are tested with deep learning approaches such as DBN, RNN, and CNN-LSTM. The deep learning approaches are chosen due to their scalability and efficiency. Deep learning approaches do not require feature engineering and are suitable to achieve desired results. The results suggest that RNN is the best approach when applied with GloVe, which had a lower error rate (KLD = 0.009) for SemEval2016 and SemEval2017 (KLD = 0.011), and lower error rate (KLD = 0.004) for STS-Gold and Sanders (KLD = 0.008) when applied with word2vec among other deep learning approaches, as shown in Table 10. Deep learning approaches outperformed the conventional and ensemble-based machine learning approaches due to their high efficacy.

Conclusions
This study contributes to the field of quantification based on sentiment analysis. The study exploits the diverse feature sets and explores the performance of machine learning approaches for the quantification of tweets. The proposed feature sets, such as POS, tweet specific, and sentiment-and content-based, increase the performance of classifiers. When the proposed feature sets are combined, they demonstrate efficient results in terms of quantification accuracy.
Three conventional machine learning approaches, namely Naïve Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM), are used in the proposed framework. AdaBoost and Random Forest are used in the case of ensemble-based approaches. Recurrent Neural Network (RNN), Deep Belief Network (DBN), and Convolutional Neural Network (CNN-LSTM) are exploited in the deep learning category of approaches. Ensemble approach AdaBoost dominated the other classifiers when applied using a single feature set, in terms of a lower error rate (KLD = 0.0213) for SemEval2016, SemEval2017 (KLD = 0.0214), STS-Gold (KLD = 0.0129), and Sanders (KLD = 0.0169). When the feature sets are combined, SVM has more promising results with a lower error rate for all four datasets, with KLD = 0.014 for SemEval2016, 0.013 for SemEval2017, 0.0051 for STS-Gold, and 0.0092) for Sanders. The computed results show that RNN with GloVe performed best for SemEval2016 and SemEval2017, and RNN with word2vec performed best for STS-Gold and Sanders.
Future work directions are as follows: • As the social web channels provide a facility to add multilingual content, it raises diverse research issues for natural language processing and context understanding.
In the case of multilingual content, especially where the diversity in different languages' structure presents issues such as sentence structure, stemming, parsing, tagging, etc., more research is needed. • Each language has its own syntax and vocabulary. Text-based features of each language provide different research challenges. Therefore, applying the proposed features and algorithms on languages such as Arabic, Persian, and Urdu will be an interesting research work, as these languages are written from right to left.

•
The analysis and learning carried out using one language can be applied to another language using cross-lingual analysis. Thus, the cross-lingual sentiment quantification task can also be a potential research area, especially in languages that lack annotated datasets.


FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques

Fingerprint evidence plays an important role in a criminal investigation for the identification of individuals. Although various techniques have been proposed for fingerprint classification and feature extraction, automated fingerprint identification of fingerprints is still in its earliest stage. The performance of traditional \textit{Automatic Fingerprint Identification System} (AFIS) depends on the presence of valid minutiae points and still requires human expert assistance in feature extraction and identification stages. Based on this motivation, we propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques (FIGO). Our solution contains two components: fingerprint enhancement tier and fingerprint identification tier. First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier. With the proposed enhancement algorithm, the fingerprint identification model's performance is significantly improved. Furthermore, we develop another existing solution based on Gabor filters as a benchmark to compare with the proposed model by observing the fingerprint device's recognition accuracy. Experimental results show that our proposed Pix2pix model has better support than the baseline approach for fingerprint identification. Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process. Two twin convolutional neural networks (CNNs) with shared weights and parameters are used to obtain the feature vectors in this process. Using the proposed method, we demonstrate that it is possible to learn necessary information from only one training sample with high accuracy.

INTRODUCTION
The presumption of innocence is a legal principle in democratic countries where all citizens are treated equally by the rule of law. In such countries, it is clearly articulated that "everyone is innocent until proven guilty". Proof of crime has vital importance for developing a fair decisionmaking mechanism. In this context, collecting clear evidence and presenting them to the competent authority is essential for the detection of crimes and criminals.
Although eyewitness identification has a long history of being used to uncover the truth about a crime, it can be considered an unreliable and problematic technique. That is because the nature of human memory is error-prone for remembering and interpreting a witnessed event. Additionally, a witness may intentionally lie under oath about a certain event in order to mislead authorities. In such cases, erroneous eyewitness identifications can lead to wrongful convictions [1]. These concerns have encouraged researchers to develop alternative solutions in the field of criminal identification for ages. As a result, experts in the criminal justice systems explored the use of biometric technology for forensic identification. Contrary to eyewitness identification, taking advantage of biometric information is more reliable and accurate since it is directly related to the unique characteristics of each individual. Behavioral characters (i.e. voice, signature), as well as physiological characters (i.e. fingerprint, iris, face), have been used for the identification and verification of criminals in the past decade.
While law enforcement agencies and intelligent communities make use of the state of the art biometric technologies for the purpose of identification, authentication, or investigation of individuals today, constructing a good biometric system has limitations and challenges. For instance, facial characteristics are typically standard and facial identification is influenced by expression, age, and observation angle to a different extent. Because of the instability of facial features, the accuracy of face recognition systems is low. When it comes to iris recognition technology, it requires advanced visual sensing cameras to capture the necessary features with sufficient details. On the other hand, fingerprints have distinctive patterns that make every fingerprint unique and more importantly they don't change over time. Therefore, fingerprint-based criminal detection systems are the most widely used biometric technology compared to other biometrics [2].
The performance of the AFIS is heavily dependent on the extraction of the quality of relevant features. These features mainly consist of minutiae points that are set of points representing the distinctive character of a person and remain constant throughout a person's lifetime [3]. Some common minutiae patterns are illustrated in Figure 1 [4]. ACE-V (Analysis, Comparison, Evaluation, and Verification) framework is used by examiners to extract minutiae points from a latent fingerprint image (collected from a crime scene) and these points are a side-by-side compared with the points taken from reference registered fingers [5]. The system automatically identifies the person if any of two sets of point pairs sufficiently mated a result of these comparisons. Nevertheless, this system does not have a standard code of conduct for identification conclusions. For example, some examiners set threshold values as between 7 and 12 for forensic identification [6]. These differences in threshold values might lead to different conclusions. Another drawback of this method is lack of standard for the definition of the region of interest for the fingerprint image and as a result, different regions of interest size can contribute to different outcomes.
In order to eliminate the manual intervention by a human expert in traditional methods, machine learning techniques can be used to automatically learn the valuable underlying implicit patterns of fingerprint images rather than hard-coded explicit features obtained by traditional methods so that it reduces the dependence on the knowledge domain [7]. However, machine learning models are datadriven approaches so they require large training examples for the application of even simple object detection tasks [8]. Unfortunately, collecting such large data samples to train a model is very challenging since every person is an unique example (fingerprint). Traditional machine learning models cannot effectively learn complex features when the model is fed with one supervised sample. In addition, retraining of the model is required when a new criminal's record is incorporated into the system, resulting in low-level usability of criminal record applications. As a solution to the limitations of the traditional machine learning model's effectiveness and usability, we present the one-shot learning approach, where the model is trained with only one sample. To achieve this goal, the proposed algorithm turns the classification problem into a difference-evaluation problem where the model attempts to learn similarity functions for image pairs instead of using manually designed features. In this way, the model gains the ability to learn from one example while also removing the need for retraining in a dynamic environment.
Furthermore, latent fingerprints, collected from a crime scene, are rarely perfect for the purpose of identification. They are maybe corrupted or incompleted. In such cases, since the important information is lost, the model can not reliably extract discriminative features from fingerprint images, therefore, it fails to achieve an adequate level of performance. Hence, a pre-processing process to enhance the fingerprint information is a critical step for the model to deliver reliable results. Previous studies mostly have used Gabor filter algorithms for high-quality fingerprint images [9], [10]. Technically speaking, Gabor filter methods use a low pass filter to connect the discontinuities in the fingerprint images to remove the noise and use a band-pass filter to create distinctive features to sharpen the images for the image enhancement process [11]. However, this process also generates unnatural (artificial) features that don't exist in the original fingerprint samples so that it greatly impedes the applicability of a successful fingerprint identification system. In order to improve the identification process, we introduce a Pix2Pix model which is based on conditional Generative Adversarial Network (cGAN) [12] by capturing the relationship between original fingerprint images and constructed version. In this way, the images are automatically reconstructed as realistic as possible from distorted or incompleted ones. This paper proposes a Fingerprint Identification approach based on a Generative adversarial network and One-shot learning technique (FIGO). The motivation of this paper, as illustrated in Figure 2, is to propose a sequential multi-model system where we synergistically bring two models together for for improving image reconstruction and identification accuracy. Our research has the following contributions: • We propose a novel combination of generic and automatic classification methods over a massive real-world fingerprint dataset. The proposed method enables effective learning from a single example in noisy environments for identification purposes. • To the best of our knowledge, this paper is the first preliminary study to suggest creating a fingerprint identification model based on a one-shot learning approach for fully automatic feature extraction and fingerprint classification so that no human intervention is required. In spite of the fact that our proposed model eliminates the limitations of existing research on fingerprint classification, the model's performance significantly degrades when low-resolution fingerprints are entered into the system as expected. • In order to improve the quality of various levels of noisy fingerprint images, we develop a Pix2Pix model structure. Experimental results indicate that our proposed technique is better suited to reconstructing fingerprint images when compared with a conventional method (Gabor filter approach) and has shown substantial improvements in fingerprint classification accuracy when incorporated into the fingerprint verification system.
The rest of this paper is organized as follows: the literature review in the context of our work is discussed in Section 2. The background information relevant to our study is reviewed in Section 3. The proposed techniques are presented in Section 4. Section 5 describes the implementation of the reconstruction fingerprint and fingerprint classification approaches. We evaluate the models' performances in Section 6. In conclusion, we finalize the paper in Section 7.

RELATED WORKS
The techniques of fingerprint identification and fingerprint feature extraction have previously been studied by several researchers. Wang et al. [13] proposed a fingerprint classification technique based on continuous orientation field and singular points. According to the authors, singular points on a fingerprint image, such as core or delta points, are important discriminative features, which were extracted using a modified Poincare index.  [16] presented an automatic minutiae algorithm where the minutiae points were efficiently detected from the NIST fingerprint database [17]. The authors then showed that their proposed method outperformed some other solutions based on the image binarization approach in terms of efficiency and robustness. Since then, minutiae-based fingerprint recognition systems have become increasingly popular due to their uniqueness. Unfortunately, singularity or minutiae points don't always exist in a fingerprint image due to the existence of noises or partial parts of the fingerprint availability. In such cases, the singularity-based or minutiae-based biometric techniques are more likely to fail to identify the target class. In order to eliminate the limitation of minutiae-based algorithms in fingerprint recognition systems, machine learning-based solutions have been proposed by some other researchers. Militello et al. [18] demonstrated the performance of pre-trained CNN architectures (AlexNet, GoogleNet, and ResNet) over two different fingerprint databases. A Bayesian classifier was implemented in [19] while support vector machines are developed in [20] for fingerprint classification. Li et al. [21] introduced a fingerprint classification based on a deep learning approach to obtain global features. The optimum parameters of the neural network model were found through a genetic algorithm. Kouamo et al. [22] proposed a model to identify the users based on their fingers using a deep neural network. Ala et al. [23] proposed a dimensionality reduction technique based on the euclidean distance between the center point and their nearest neighbor bifurcation minutiae. By doing so, redundant and irrelevant features were eliminated. The remaining topmost features were then used to develop a fingerprint identification method based on a backpropagation neural network. Using the FVC 2002 database, they evaluated their methods and found that their results were superior to those of several other methods. In the literature, the primary problem with implementing an efficient fingerprint application using machine learning models is the lack of training samples since each individual has only one unique object. The above-mentioned studies attempted to circumvent this issue in some way by utilizing data augmentation techniques [24], [25]. However, augmented data created from existing data are often noisy. In many cases, machine learning models struggle to distinguish between noisy and noise-free data, which reduces the performance of the model [26]. Moreover, for each new sample, both stateof-the-art machine learning algorithms as well as transfer learning-based possible viable solutions are required to be retrained. Instead of synthesizing new fingerprint samples, we propose a one-shot learning approach to train the model with one training sample. Our proposed technique distinguishes fingerprints from each other more efficiently and eliminates the need for retraining the whole system.
As we outlined previously, fingerprints are rarely of good quality. Due to the presence of noise, they are distorted and corrupted. Therefore, it is essential to incorporate a fingerprint enhancement technique into the fingerprint identification system to make the system robust and increase identification accuracy. Barnouti et al. [27] proposed histogram equalization and compression methods in order to both increase the image quality and the processing speed. Using histogram equalization, the authors were able to increase the quality of the images while using principal component analysis to reduce the dimensions without losing much information in order to speed up the verification process. However, histogram equalization causes a change in the brightness of an image resulting in a considerable loss of image detail. Wang et al [28] initially found an area that consists of a singular point then offered to use a bandpass filter in the Fourier domain for image enhancement. It is, however, difficult to extract singular points from a poorquality fingerprint image.

BACKGROUND
In this section, we review background information related to our research.

CNN Model :
CNN is a special type of neural network which mostly applied to extract relevant features for the image content [29], [30], [31], [32]. Instead of extracting handcrafted explicit features by the classical image recognition system, a CNN model is able to learn the valuable features automatically for better classification [33].
Technically speaking, a CNN model consists of input, output, and hidden layers. The hidden layer has three main components known as the convolutional layer, the pooling layer, and the fully connected layer [34]. While in the convolutional layer, raw pixel data is converted to a feature map that detects all different patterns in the input data, in the pooling layer the feature map is being downsampled to select the most valuable information. In the fully connected layer, this joint information content is used to classify the given image into various classes.

Overview of Fingerprint Recognition System :
The typical fingerprint recognition system consists of two stages, enrollment and identification [35]. Figure 3 demonstrates the traditional fingerprint identification system. The biometric device scans the individual's fingerprints and converts them into digital images during the enrollment process. After that, the unique features of the fingerprint images are extracted and stored in a database. When a fingerprint is presented at the identification stage, it is subjected to the same process as at the enrollment stage in order to capture distinctive features and compare them against a database of known individuals. If any fingerprint pair is sufficiently similar, the identification is successful.

One-shot Learning Approach :
One-shot learning approach was firstly introduced in [36]. This technique is directly inspired by the ability of human beings to learn new tasks from the past gained knowledge by using particular models/algorithms. While traditional machine learning models require millions of training examples in the training process, the main idea of the oneshot learning approach is to reduce the number of training samples needed to perform machine learning tasks by learning the similarities and differences between object categories [37] [38].Therefore, machine learning models can be generalized well without a need for a vast amount of supervised information (even with a single example) in the one-shot learning setting. In some cases, collecting sufficient data is infeasible or expensive. In such application scenarios, such as fingerprint matching applications, the implementation of a machine learning model via one-shot learning modeling would be more suitable to achieve remarkable learning results.

Conditional Generative Adversarial Network :
The Generative Adversarial Network (GAN) is a class of machine learning/deep learning models in which two neural networks compete in a zero-sum game [39], [40]. In this game theory, one network's gain is another network's loss. Although GANs were originally proposed as a form of generative modeling under the unsupervised learning paradigm, they have also proven useful under semi-supervised learning, fully supervised learning, and reinforcement learning.
The core idea behind GANs as a model is that it is comprised of two networks, also known as Generator and Discriminator. The generative network generates candidates while the discriminator evaluates them. The generative network learns to map from a latent space to a data distribution of interest, while the discriminative network distinguishes candidates produced by the generator from the real data distribution. Generative networks are trained to fool discriminator networks by producing novel candidates that are as real as possible, such that the discriminator is unable to distinguish between real and synthetic samples.
The GAN is capable of generating quality synthetic data in a variety of situations, however, in some cases, it does not produce the desired outputs as there is no control over the types of data that are produced by the generator. To overcome the shortcomings of GAN, cGAN was introduced in [12]. Using this approach, auxiliary information can be used to guide the data generation process. Additional information may include supervised samples or partial parts of the data that learn a better representation of target images.

THE PROPOSED APPROACH
In the following subsections, we first describe the proposed fingerprint reconstruction method using the Pix2Pix model to improve the pattern legibility that can help to enhance the fingerprint classification reliability. We then present another existing solution based on Gabor filters as a benchmark to compare with the proposed model by observing the fingerprint identification model's accuracy. Last but not least, we discuss our unique and reliable method for fingerprint identification based on the one-shot learning approach. Note that Pix2Pix and one-shot learning models are the two essential components of the FIGO model.

Fingerprint Reconstruction Method Using Pix2Pix Model:
As we outlined earlier, latent fingerprints obtained from crime scenes are largely corrupted due to external factors such as the type of surface on which latents are found, the force of contact with the object, skin conditions, and so on. In order to improve the quality of latent fingerprint images, most latent fingerprint images must be preprocessed. In this regard, we propose a new fingerprint reconstruction framework based on the Pix2Pix model inspired by [41]. Our proposed Pix2pix model is based on cGAN where the model learns automatically how efficiently convert noisy/incomplete fingerprint images to enhanced fingerprint images by translating pixels to pixels. To this end, we adapt two neural network models based on CNN, labeled discriminator (D) and generator (G) to learn the translation function.
In the beginning, G takes low-resolution fingerprint images as input and produces output, which is then sent to D. When D receives distorted fingerprint images and outputs from G, it compares them with the same distorted fingerprint images and the original fingerprints. The two models are trained simultaneously. The G attempts to not only fool the D, but also translate distorted images into the desired restored images, while the D is able to detect G's fake samples. By fine-tuning G parameters, the real and fake distributions of data become highly similar. At this point, the enhanced fingerprint images are successfully produced from the noisy fingerprint images through the proposed translation model. As shown in Figure 4, the proposed Pix2Pix model successfully translates noisy images to enhanced counterparts.

Fingerprint Reconstruction Method Using Gabor Filters :
In [42], Dennis Gabor first introduced the Gabor filter, which was later extended by David Daugman in [43]. By taking into account its frequency-selective and orientation-selective properties, the Gabor function is a very useful technique for capturing both time and frequency information simultaneously from digital images. It has therefore been widely used in computer vision and image processing for the analysis of textures, the detection of edges, the extraction of features, and so on. Hong et al. [44] assumed that periodic parallel ridge and valley patterns, which are the primary features of a fingerprint image for the extraction of minutiae points, could be described as sinusoidal plane waves. Since a Gabor filter is represented as a complex sinusoidal signal modulated by a sinusoidal plane wave [45], it makes them more suitable in the fingerprint image enhancement process based on their assumption. Therefore, the authors deployed the Gabor filters in an automatic fingerprint identification system to remove undesired noises and reported promising results. Since then, Gabor filters have become one of the most dominant approaches for addressing problems related to noisy or partial patterns in the literature of fingerprint enhancement methods. Figure 5 demonstrates how a noisy image is reconstructed to improve the image quality using the Gabor filter with our implementation.
Yang et al. [46] demonstrated that fingerprint images do not always follow the ideal sinusoidal plane wave shape (see Figure 6) in which case the technique does not improve the quality of these images. Thus, Hong and others' assumption is not valid in all circumstances. This technique has another disadvantage in that Gabor filters are parametric solutions that require a set of parameters to be tuned, such as the standard deviations of the Gaussian function, the convolution mask size, etc. Its denoising performance is dependent on parameter values, and these values can only be determined through experience. For example, larger standard deviations tend to make the method more robust to noise, but it is also more likely to generate spurious minutiae. On the other hand, a large number of real minutiae points would be ignored with smaller values of the standard deviation. Consequently, the technique leads to an imagedependent solution due to the parameter selection process.
To provide a more robust noise reduction algorithm while preserving the true ridge and valley structures without depending on fingerprint images, we propose a Pix2Pix model. Our experimental results demonstrate that our Fig. 6: The right-top side of the image can be well approximated by sinusoidal plane waves, while left-bottom side can not be characterized by sinusoidal plane waves [46]. Pix2pix model is more powerful than traditional Gabor filters in reconstructed fingerprint images. In Section 6, we analyze the model performances in detail.

Fingerprint Identification Using One-Shot Learning Approach :
To develop a fully automated fingerprint classification system, we propose a similarity-based learning approach in which the model attempts to learn similarity functions for image pairs as opposed to using manually designed features. This idea is inspired by the Siamese network structure [47]. The proposed model consists of two twin CNN networks connected by a similarity layer at the top. To put it another way, these two identical networks have the same configuration (i.e., the same number of layers and nodes) that share weights and parameters. The tying of the weights ensures that similar images will map close together in the feature space while dissimilar pairs will fall apart since each network in the architecture computes the same function.
In order to develop a model for fingerprint image classification, we generate all possible fingerprint image pairs from original samples and label them either as 'positive' or 'negative'. If two images are the same, then the image pair is labeled as 'positive', otherwise, it is labeled as 'negative'. During training, matching and non-matching image pairs are fed to CNN models to extract unique features from labeled examples. After that, the Euclidean distance is calculated between all image pairs. As a result of these calculations, a loss function is derived. The loss function is designed to minimize the squared Euclidean distance between positive samples and maximize for negative samples. In this way, the proposed approach gains discriminative and generalization ability. With its discriminative ability, the model can efficiently differentiate a fingerprint from the others. On the other hand, due to its generalization ability, the proposed model properly identifies the person even if there is variations in the fingerprint image due to the presence of structured noise. The training process of the proposed fingerprint identification method is depicted in Figure 7.
Mathematically speaking, let M be our fingerprint identification model, x represents any fingerprint image and Y represents the corresponding ground-truth output label The distance metric is calculated based on element-wise absolute difference: The design of this proposed fingerprint identification algorithm using the one-shot learning approach differs considerably from traditional machine learning techniques. The advantages of the proposed approaches are twofold. Firstly, traditional machine learning models require a huge amount of training samples whereas this model can be trained with a single training sample. Another notable advantage of this method is that when a new user joins/leaves the fingerprint database there is no reason to retrain the model because the model is trained to distinguish between fingerprints based on pairwise similarity. However, with a typical machine learning classification model, the system has to be retrained with each new user, reducing the usability of such systems.

MODEL IMPLEMENTATION
In this section, we describe the dataset used to build the fingerprint identification model using generative adversarial networks and one-shot learning techniques, along with an explanation of how the FIGO model is implemented.

Dataset:
In our project, we use the SOCOFing dataset in [48]. The dataset contains 6,000 fingerprints taken from 600 Africans. A fingerprint sample is taken from each finger of each individual. The noise was created using three different alteration techniques, including obliteration, central rotation, and zcut, using the STRANGE toolbox [49]. Based on parameter settings in this toolbox, the authors generated different levels of noise and categorized them as 'easy', 'medium', and 'hard'. The output of this phase is shown in Table 1 as an example. As seen in Table 1, the original image is slightly   distorted by noise in the 'easy' category. When the level of noise is increased in the 'medium' category, the quality of the image is reduced. In addition, the more additive noise corrupts the real image to a great extent in the 'hard' category. In this case, the fingerprint image will lose much of its most important information.
The image file format is illustrated in Figure 8: where: 1) identifies each individual from 1 to 600.
2) indicates the gender of the individual, such as Ffemale, M-male.

FIGO implementation:
As previously mentioned, the FIGO model consists of a Pix2Pix model and a fingerprint identification model. During the training process, each model is trained separately. Once the models are completely trained, they are linearly stacked one on top of another into a pipeline where the output of the Pix2Pix model is fed to the proposed automated fingerprint identification model for the identification. We implement both models using Python programming language and the Tensorflow library. The SOCOFing dataset is randomly divided into three parts as training, test, and verification. We use 80% of the dataset to train the models, 10% of the dataset for testing, and the remaining is reserved for evaluation of the FIGO's performance. The Python code runs on Google Colab equipped with NVIDIA GPU Tesla P4 [50].
To develop a denoising framework based on the Pix2Pix model, we utilize the same architecture and parameters for both the discriminator and generator except for types of loss functions. A "Mean Absolute Error" is used to measure the error of the generator, whereas a "Mean Square Error" is used to measure the error of the discriminator. Table 2 shows the fine-tuned system parameters for the Pix2PiX model experiment. We also implement a novel one-shot learning approach for the identification of criminals. A fine-tuned set of system parameters is provided in Table 3. As a brief summary, in our scenario, we are using only real (undistorted) samples in order to train a fingerprint identification model using a one-shot learning technique. The assumption is regarded as more realistic for crime scene investigation, since the fingerprint information of every or almost every citizen is stored in the fingerprint database and is always available to authorities. The use of this approach can also help ease the burden of collecting or generating large quantities of fingerprint information. In the process of training the model, negative and positive pairs of data are created, which are then used to train the model based on triplet loss [51]. The purpose of the triplet loss is to ensure that the distance between positive samples is as small as possible while the distance between negative samples is as large as possible. In this way, the proposed fingerprint identification model is able to effectively distinguish latent objects from each other.  Fig. 9: The fingerprint identification model's recognizing accuracy under various corruption level without incorporating fingerprint enhancement algorithms.
an enhanced version. Each image from the real dataset is paired with this enhanced image one by one and fed into the fingerprint identification model. For each match, the fingerprint identification model computes a similarity score.
The system determines the user's identity with the highest score according to this comparison.

EVALUATION AND RESULTS
To assess the proposed FIGO model's overall performance in terms of image quality enhancement and recognition accuracy, we have conducted several experiments. In the first set of experiments, fingerprint enhancement algorithms are not applied. Once the fingerprint identification model is trained with only real samples, the model is tested with samples under three different corruption levels along with real ones. Figure 9 illustrates the unimodals' recognition accuracies at different noise levels. In our study, we found that the model classifies each real (reference) fingerprint image almost precisely. However, when the images are distorted by small alterations, the accuracy rate of the model drops to 85.93%. The reason for this is that the amount of distinctive information included in the images is limited. Furthermore, when the fingerprint image degeneration is severe to a certain extent, it leads to very poor accuracy. In cases of heavy noise contamination in images, since most of the valuable rich information from clean samples is lost, the model's identification performance is significantly reduced with an accuracy rate of 22.8%.
In the second set of experiments, we extend the experiment for a more comprehensive evaluation by integrating the fingerprint enhancement algorithms (Pix2Pix model and a Gabor filter) with the fingerprint verification model in order to eliminate the adverse effect of low-quality samples. As shown in Figure 10, both enhancement methods help improve the recognition accuracy under actual sampling conditions and the Pix2Pix model gets a bit more encouraging result than the Gabor filter method, which are 100% and 99.8% respectively. The slight advantage of the Pix2Pix model over the Gabor filter algorithm could probably be explained that the Pix2Pix model brings out more discriminative information of the disguised latent fingerprint images.  Fig. 11: The fingerprint identification model's recognizing accuracy under various corruption level by combining Gabor filter and Pix2Pix fingerprint enhancement algorithms.
In the case of the existence of different levels of corruption, the multimodal system including with Gabor filter method performs worse than the unimodal fingerprint identification system that has the accuracy of 75.2%, 27.8%, and 14.39 for easy, medium, and hard categories respectively. This consequence mainly results from the fact that although the Gabor filter algorithm helps suppress the undesired noises, a significant number of spurious features are generated, which leads to unsatisfactory accuracies. On the other hand, the multimodal system including with Pix2Pix model (FIGO model) helps speed up fingerprint detection in all three different noisy scenarios due to its effective noise reduction method. As can be seen from Figure 10, the proposed FIGO model increases the recognizing accuracy nearly by 12% and 17% for easy and medium groups respectively. When the fingerprint image distortion is severe to a great extent, it is the most challenging for unimodal fingerprint identification to detect useful information whereas, the designed FIGO approach boosts the accuracy of fingerprint identification by approximately 40%.
In the last set of experiments, we combine the Gabor filter method and Pix2Pix model to enhance fingerprints, where the output of the Gabor filter technique is fed to the Pix2Pix model before the input images are entered into the fingerprint verification model. As it can be observed in Figure 11, the matching performance of the fingerprint recognition system is slightly increased with the combined scenario for noise-free images. While the design model performs worse than unimodal recognition in the case of noise, the accuracy of fingerprint identification has improved compared to Gabor-based enhancement. Due to the fact that the Gabor filter method unexpectedly generates artifacts and does not provide sufficient enhancement in some areas of the fingerprint images, this leads to a degradation of the accuracy of fingerprint identification.
Overall, the results of the experiments indicate that the accuracy of the proposed fingerprint verification system based on the one-shot learning approach is very high with high-quality fingerprint images, but its performance decreases more or less with distorted images. In our empirical study, the Gabor filter method is not a desirable approach for the image enhancement process. Moreover, the proposed Pix2Pix model is an effective way of handling different levels of noise and is more powerful than Gabor filter approaches for enhancing fingerprint images, so that it can be incorporated into the identification process as a complementary tool.

CONCLUSION
When a decision is made regarding a quilt, the criminal justice system relies heavily on forensic evidence. The use of false or misleading forensic evidence contributes to erroneous convictions that lead to the breakdown of trust and injustice. Latent fingerprints are one of the most important pieces of evidence that have primarily been used for forensic purposes. However, in practice, a large percentage of fingerprint images obtained are of poor quality and require significant restoration. In addition, the accuracy and reliability of the fingerprint identification system are dependent upon the expertise of the latent print examiners. The FIGO model is designed to ensure robust feature extraction and accurate feature matching without the involvement of a human expert. The FIGO is multimodel where the Pix2Pix model is added to the proposed fingerprint classification model based on the one-shot learning approach. According to the results of numerous experiments, the proposed image enhancement algorithm significantly improves the performance of the proposed biometric system, resulting in remarkable identification accuracy.