Neural Network-Based Expression Recognition System for Static Facial Images

Affective Computing is a field of studying the human effect to interpret, recognize, process, and simulate in computer science, psychology, and cognitive science. Humans express their emotions in a variety of ways such as body gesture, word, vocal, and mainly facial expression. Non-verbal behavior is a significant component of communication, and facial expressions of emotions are the most important complex signal. Facial Expression Recognition (FER) is an interesting and challenging task in artificial intelligence. FER system in the study three steps including preprocessing, feature extraction and expression classification. In the paper, comparative analysis of expression recognition is implemented based on Neural Network (NN) with three feature extraction methods of Sobel Edge, Histogram of Oriented Gradient and Local Binary Pattern. NN-based expression recognition system achieves an accuracy of 95.82% and 97.68% for JAFFE and CK+ dataset respectively. The result has shown that the Edge features are the effected features for recognizing human expression using still images

Survey on Emotion Recognition Using Facial Expression

Automatic recognition of human affects has become more interesting and challenging problem in artificial intelligence, human-computer interaction and computer vision fields. Facial Expression (FE) is the one of the most significant features to recognize the emotion of human in daily human interaction. FE Recognition (FER) has received important interest from psychologists and computer scientists for the applications of health care assessment, human affect analysis, and human computer interaction. Human express their emotions in a number of ways including body gesture, word, vocal and facial expressions. Expression is the important channel to convey emotion information of different people because face can express mainly human emotion. This paper surveys the current research works related to facial expression recognition. The study attends to explored details of the facial datasets, feature extraction methods, the comparison results and futures studies of the facial emotion system

Angled local directional pattern for texture analysis with an application to facial expression recognition

Local binary pattern (LBP) is currently one of the most common feature extraction methods used for texture analysis. However, LBP suffers from random noise, because it depends on image intensity. Recently, a more stable feature method was introduced, local directional pattern (LDP) uses the gradient space instead of the pixel intensity. Typically, LDP generates a code based on the edge response values using Kirsch masks. Yet, despite the great achievement of LDP, it has two drawbacks. The first is the static choice of the number of most significant bits used for LDP code generation. Second, the original LDP method uses the 8‐neighborhood to compute the LDP code, and the value of the centre pixel is ignored. This study presents angled local directional pattern (ALDP), which is an improved version of LDP, for texture analysis. Experimental results on two different texture data sets, using six different classifiers, show that ALDP substantially outperforms both LDP and LBP methods. The ALDP has been evaluated to recognise the facial expressions emotion. Results indicate a very high recognition rate for the proposed method. An added advantage is that ALDP has an adaptive approach for the selection of the number significant bits as opposed to LDP