Emotion Recognition from Facial Expressions using Neural Networks
Automatic facial expression recognition is the kernel part of emotional information processing. The regions of interest for extracting the features from the facial images include eyes, eyes with eyebrows, mouth. In this paper there are two main parts locating the points in the face region to form graph based features and training the neural network to recognize the emotions from the corresponding feature vector. For the first phase, sixteen points are manually located to create graph with edges connecting such points. Subsequently, the Euclidean distances from those edges are calculated and defined as features for training in the next phase. The next phase is using Feed forward networks (FFN) a kind of artificial neural network (ANN) with back propagation algorithm to recognize the emotions like excitement, happy, shocked, confusion, and disgust. Facial expression analysis goes well back into the nineteenth century. Emotion recognition plays an important role in human interaction. Facial expressions may be expressed differently by different people. Facial expression recognition becomes an interested topic for researchers because it is the human body language to increase understanding in interaction between human and human, human and computer. The psychologist Mehrabian said "The facial expression sends the 55% information during the process of human communication while language sends only 7%" (2) As the points are located on the face and Euclidean distances are calculated between the edges connecting the points, if the facial point markers are sufficient then the recognition accuracy will be high. However if the number of facial point markers is too high then the over fitting and time consumption problems will occur. If the number of facial point markers is too low then the recognition will not be good. In this work, the high accuracy recognition system based on machine learning with a reasonable number of samples (images) and sufficient amount of facial points are introduced. These number of facial points are sixteen including inner eyebrow of two points, middle eyebrow of two points, outer eyebrow of two points, inner eye of two points, outer eye of two points, mouth of four points and two of philtrum. These points are defined as vertices of graph. Then connecting edge is formed from a pair of vertices along with its Euclidean distance. The distances are interpreted as features that send to the learning process to recognize the expressions like excitement, happy, shocked, disgust and confusion using Artificial neural networks(ANN) MATLAB is used as tool in this paper.