Recognition of Facial Emotion Using Swarm Optimization and Component Analysis

Rohit Agarwal and Himanshu Sharma

The advancements in the pattern recognition technologies made the recognition of face as a most attractive filed of interest. Under certain conditions, a level of maturity is reached by the face recognition systems. The variations in internal and external parameters may affect the performance of the algorithms used for face recognition. In human-machine interactions, identification of emotions by an automated system plays a vital role. In order to deal with the frameworks of machine and human, emotion reflection and its understanding development are very crucial. To differentiate various emotions of face, emotion recognition system based on Artificial Bee Colony (ABC) optimization is proposed in this paper. Feature vectors are extracted using Independent component analysis (ICA) and HoG or SIFT feature extraction technique. These extraction techniques produces high rate of accuracy with less error probability. The emotion classifications are obtained by testing process. Accuracy, false rejection rate and false acceptance rate are used to measure the performance.

Volume 11 | 10-Special Issue

Pages: 603-609

DOI: 10.5373/JARDCS/V11SP10/20192848