Emotion Recognition using Gamma Correction Technique Applied to HOG and LBP Features

Human social interaction, especially facial expressions, is often influenced by non-verbal communication. The surrounding people often watch the face in day-to-day group interaction to understand the inner feelings of a person. Face thus forms an essential source of human emotion recognition that is generally categorized as a surprise, fear, anger, disgust, sad, and happy. Recognition of emotions plays an important role in a variety of fields in behavioral science. In this paper, median filtering is used for pre-processing of an input image. Watershed segmentation is used before extracting features to obtain the necessary image properties. Gamma correction is implemented in this paper, and features are obtained, including Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) techniques. The performance of LBP and HOG is evaluated. kNN and SVM are used as classifiers for comparing the efficiency of recognition. The overall accuracy, along with precision, recall, and f-score has been computed and compared. It is found that all performance parameters with gamma correction give better performance compared to without gamma correction.