Facial Emotion Verification by Infrared Image

Recognition and classification of face-emotion is a vital issue now a day. Emotion bears a resemblance to the people's thought process and based on that a mapping of anyone's activity is possible to establish by analyzing facial expressions. Facial emotion is recognized based on interaction or appearances of eyes, chick, forehead, lips as well as from the whole face in different forms. In this paper, facial emotion is recognized and classified them to create infrared thermal face image data-mask and tried to correlate them based on the variances and standard deviation with EPDF (Enhanced Probability Density Function) of the identified images. During the testing and recognition process, a centralized stored data has been is used to avoid redundancy of data to be stored after recognition. In this experiment, three types of emotions are taken into account and their infrared thermal facial images are recorded simultaneously. In the processing, a calibration procedure is adopted to reduce the variances produced by dissimilar image-set from the same face due to independent parts of a face analysis that are related to facial emotions. Features are taken out from pixel values of classified images. The investigational results of facial images confirmed that the proposed system attained 91.73% accuracy in identification in RGB and 92.39% in infrared images respectively. Whereas as per D. Kumar et al, it is 65% and M. A. Eid has achieved 85% accuracy on identification. The average detection is 91.73% with eight RGB images. Whereas detection from the eight infrared images the average detection rate is 92.39%. This exits the robustness of the suggested methods.