Real-time emotion identification for socially intelligent robots

Abstract

This paper presents a real-time emotion recognition system (RTERS) as a first step towards developing a socially intelligent robot. The RTERS first localizes faces in a sequence of images, then features are extracted and passed to a recognition engine that codes facial expressions into one of seven different emotional states: happiness, sadness, fear, disgust, anger, surprise, and neutrality.We propose and develop a distance-based classifier, called Distance-Ratio Classifier, for emotion identification from the feature vectors. The performance of the proposed distance-ratio classifier was compared with support-vector-machine-based classifiers, using different feature extraction and dimensionality reduction approaches, including principal component analysis, linear discriminant analysis, kernel principal component analysis, greedy kernel principal component analysis, and generalized discriminant analysis. Extensive computer simulations were conducted to illustrate the performance of the proposed RTERS. Using two widely used databases for performance evaluation, the best performance of the proposed RTERS was 95.8% using the generalized discriminant analysis for dimensionality reduction and the proposed distance-ratio classifier.