Survey on facial expressions recognition: databases, features and classification schemes

The recognition of facial expression in images is one of the motional states in the observing forms and is one of the most frequent non-verbal routes in which a person transfers his inner emotional expressions on faces. The recognition of facial expressions in a wide range of fields including psychological and legal studies, animation, robotics, lip-reading, image and video conferencing, communications, telecommunications, and security protection whilst counterterrorism is used to identify individuals as well as human-machine confrontation. The general solution to this problem includes three general steps: images preprocessing, features extraction, and expression classification algorithms. A series of pre-processing steps must be performed to process the area on the face and then detect the expression, that is, a square in the face must be localized while the rest of the image must be removed. Then, Features extraction is used to classify. Each facial expression to a specific category. We divide our data, including images from different expressions, into two parts: training and testing. Different categories have been learned to specify different features that tested thereafter. In recent years, a number of researches has been performed on a facial expression analysis. Even though much progress has been made in this field since the recognition of facial expression with a high accuracy rate is difficult to achieve due to the complexity and variability. In this research article, we noticed that most of researchers are used JAFFE and CK+ databases due the diversification and high accuracy. Nevertheless most of researchers are used PSO, PCA, and LBP features as well as HOG that presented high accuracy. We also noticed that SVM and CNN classification algorithms have been used mostly due to high accuracy and response latency with few errors.