Answers.Body: <p>Multi Layers Perceptron(MLP) can be used for image classification, but it has a lot of deficiency than Convolutional Neural network(CNN). But if you compare <strong>MLP</strong> and <strong>Fisher Faces</strong> , the better one is <strong>MLP</strong>, because Fisher Faces will be increasingly difficult if adding more individuals or classes. You can make a simple MLP model, because it just has 3 layers which are an input layer, hidden layer and output layer, here a source code that you can try:</p>
  
  <ul>
  <li><a href="https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f" rel="nofollow noreferrer">https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f</a></li>
  </ul>
  
  <p>if you make a model, it will be based on training data, I think if make data trained composed based on noise in background and different light shadows on your image I think it will have a better performance, but remember if you are using <strong>MLP</strong> for image classification it can just predict an image on one spot, for example:
  "if you train a model with the object in the middle of an image, your model can not predict it when the image is moved to the different spot".</p>
  
  <p>here is pdf to see Fisher Faces performance:</p>
  
  <ul>
  <li><a href="https://core.ac.uk/download/pdf/84801048.pdf" rel="nofollow noreferrer">https://core.ac.uk/download/pdf/84801048.pdf</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-21T12:58:08.163Z
Answers.Text: for some reason yes :) ,but i suggest u to use CNN for image Classification, it have better performance
Answers.UserDisplayName: Firhan maulana rusli
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-21T14:10:49.863Z
Answers.Text: I will convert MPL network to C code. That's why I wonder if MPL can be used for image classification. Else I will go with fisher faces.
Answers.UserDisplayName: euraad
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-21T14:15:07.070Z
Answers.Text: Try Deeplearing2C on GitHub. I made it.
Answers.UserDisplayName: euraad
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-21T11:19:35.103Z
Answers.LastActivityDate: 2020-04-22T14:54:32.800Z
Answers.LastEditDate: 2020-04-22T14:54:32.800Z
Answers.UserDisplayName: Firhan maulana rusli
Answers.Body: <p>Depends, if the faces are centered and have the same background yes. You also need a lot of data.</p>
  
  <p>If they are daily life images, then no. You will have very bad generalization.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-21T14:40:10.927Z
Answers.LastActivityDate: 2020-04-21T14:40:10.927Z
Answers.UserDisplayName: FourierFlux
Answers.Body: <p>let me try to answer your question. yes, you can use multilayer perceptron to image classification. Multilayer Perceptron is topology
  the most common of ANN, where perceptrons are connected to form layers. An MLP has
  input layer, at least one
  hidden layer, and output layer.
  Multilayer perceptron is one method
  many used. one of them, regards research on classification
  human skin based on its color, Khan
  (Khan, Hanbury, Stöttinger, &amp; Bais, 2012)
  compare the nine methods for
  classifications include BayesNet, J48, Multilayer Perceptron (MLP),
  Naive Bayes, Random Forest, and SVM.
  The results show that the Multilayer Perceptron (MLP)
  produce the highest performance after
  Random Forest and J48.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-21T09:23:03.613Z
Answers.LastActivityDate: 2020-04-21T09:23:03.613Z
Answers.UserDisplayName: Muhammad Faiq A.
Body: <p>I'm seeking guidence here.
  Can I use Multi Layers Perceptron (MLP), e.g regular flat neural networks, for image classification?</p>
  
  <p>Will they perform better than Fisher Faces?</p>
  
  <p>Is it difficult to do image classification with a MLP network? </p>
  
  <p>It's on basic level like classifying objects and not detailed structures and patterns. </p>
  
  <p>Important to me is that the MLP need to be trained with pictures that can have noise in background and different light shadows.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-20T02:37:46.773Z
Comments.Text: Hi @Daniel, welcome to Artificial Intelligence Stack Exchange, Please [see the tour](https://ai.stackexchange.com/tour) and look around to see how this site works. For your questions, could you please ask one question per post? it will give more focus on each post
Comments.UserDisplayName: malioboro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-20T05:11:54.547Z
Comments.Text: I highly recommend for any image classification you look into convolutional neural networks. These should preform much much better than a standard MLP for image classification. Yes while you can do it with MLP, you probably shouldn't. The main issue comes from the input layer, feeding in each pixel as a giant 1D vector might give you too many parameters to train and an overall inferior model...
Comments.UserDisplayName: PMaynard
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-21T11:12:26.780Z
Comments.Text: @PMaynard Thank you. Now I know that MLP is bad practice in image classification. Perhaps I should use Fisherfaces instead?
Comments.UserDisplayName: euraad
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-04-22T01:30:46.417Z
Comments.Text: No use a convolutional neural network, Unless this is an assignment or task that requires it you should almost undoubtedly use a convolutional neural network. Generally for image classification I would say, CNN = Great, MLP = poor/okay, Fisher Faces = Bad.
Comments.UserDisplayName: PMaynard
Tags: <classification><image-recognition><multi-label-classification>
Title: Can I do image classification with Multi Layers Perceptron (MLP)?
UserDisplayName: euraad
ThreadText: # Can I do image classification with Multi Layers Perceptron (MLP)?
  
  (Asked by: username_0 on 2020-04-20)
  
  I'm seeking guidence here.
  Can I use Multi Layers Perceptron (MLP), e.g regular flat neural networks, for image classification?
  
  Will they perform better than Fisher Faces?
  
  Is it difficult to do image classification with a MLP network?
  
  It's on basic level like classifying objects and not detailed structures and patterns.
  
  Important to me is that the MLP need to be trained with pictures that can have noise in background and different light shadows.
  
  **Tags:** classification, image-recognition, multi-label-classification
  > **Comments:**
  > **username_1**: Hi @Daniel, welcome to Artificial Intelligence Stack Exchange, Please [see the tour](https://ai.stackexchange.com/tour) and look around to see how this site works. For your questions, could you please ask one question per post? it will give more focus on each post  
  > **username_2**: I highly recommend for any image classification you look into convolutional neural networks. These should preform much much better than a standard MLP for image classification. Yes while you can do it with MLP, you probably shouldn't. The main issue comes from the input layer, feeding in each pixel as a giant 1D vector might give you too many parameters to train and an overall inferior model...  
  > **username_0**: @PMaynard Thank you. Now I know that MLP is bad practice in image classification. Perhaps I should use Fisherfaces instead?  
  > **username_2**: No use a convolutional neural network, Unless this is an assignment or task that requires it you should almost undoubtedly use a convolutional neural network. Generally for image classification I would say, CNN = Great, MLP = poor/okay, Fisher Faces = Bad.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_3 on 2020-04-21. Score: 2 | Accepted answer
  
  Multi Layers Perceptron(MLP) can be used for image classification, but it has a lot of deficiency than Convolutional Neural network(CNN). But if you compare **MLP** and **Fisher Faces** , the better one is **MLP**, because Fisher Faces will be increasingly difficult if adding more individuals or classes. You can make a simple MLP model, because it just has 3 layers which are an input layer, hidden layer and output layer, here a source code that you can try:
  
  * <https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f>
  
  if you make a model, it will be based on training data, I think if make data trained composed based on noise in background and different light shadows on your image I think it will have a better performance, but remember if you are using **MLP** for image classification it can just predict an image on one spot, for example:
  "if you train a model with the object in the middle of an image, your model can not predict it when the image is moved to the different spot".
  
  here is pdf to see Fisher Faces performance:
  
  * <https://core.ac.uk/download/pdf/84801048.pdf>
  
  > **Comments:**
  > **username_3**: for some reason yes :) ,but i suggest u to use CNN for image Classification, it have better performance  
  > **username_0**: I will convert MPL network to C code. That's why I wonder if MPL can be used for image classification. Else I will go with fisher faces.  
  > **username_0**: Try Deeplearing2C on GitHub. I made it.  
  
  ---
  
  ### Answer by username_4 on 2020-04-21. Score: 1
  
  Depends, if the faces are centered and have the same background yes. You also need a lot of data.
  
  If they are daily life images, then no. You will have very bad generalization.
  
  
  ---
  
  ### Answer by username_5 on 2020-04-21. Score: 1
  
  let me try to answer your question. yes, you can use multilayer perceptron to image classification. Multilayer Perceptron is topology
  the most common of ANN, where perceptrons are connected to form layers. An MLP has
  input layer, at least one
  hidden layer, and output layer.
  Multilayer perceptron is one method
  many used. one of them, regards research on classification
  human skin based on its color, Khan
  (Khan, Hanbury, Stöttinger, & Bais, 2012)
  compare the nine methods for
  classifications include BayesNet, J48, Multilayer Perceptron (MLP),
  Naive Bayes, Random Forest, and SVM.
  The results show that the Multilayer Perceptron (MLP)
  produce the highest performance after
  Random Forest and J48.
  
  
  ---
  
  