Emotion recognition from facial expressions using GFE, LBP and Hog feature extraction techniques

Abstract

The social interaction of human beings is many times influenced by non-verbal communication, especially facial expressions. In societal life face of a human being is mostly observed by surrounding people to know the inner feelings. Thus, face forms a significant source of expressing human emotions, typically categorized into surprise, anger, fear, disgust, sad and happy. In the variety of behavioral science fields, emotion recognition has a significant role to play. The present paper describes a system in which preprocessing is performed by median filtering. Before extracting features, the watershed segmentation is applied to get the required characteristics of an image. In this paper, the Gamma based Feature Extraction (GFE), Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) technique have been used for feature extraction. The LBP algorithm is additionally tested with and without application of gamma correction using GFE. Two classifiers, namely kNN and SVM, have been employed, and their performance is compared. kNN and SVM, being supervised classifiers, can aid in better accuracy with proper training.