Performance analysis of single and combined bit-planes feature extraction for recognition in face expression database

Bit-planes for digital gray images have been used in many applications for special feature extraction. This paper presents analysis of single and combined bit-plane performance for face recognition. Novel approach of using bit-plane as input to Feedforward Neural Network is used. Comparison is done to analyze recognition rate of using single and combined bit-planes. Every single pixel in an 8-bits gray level digital image consists of 8 bits. Among these eight bit-planes, bit-planes 4, 5, 6 and 7 provide better recognition rates than bit-planes 0, 1, 2 and 3. Feed-Forward Neural Network is used in this paper for training and testing the bit-planes. The face database used for evaluation is CMU AMP Face Expression Database, which consists of 13 subjects, with 75 8-bits gray level images from each subject. Bit-planes 4, 5, 6 and 7 achieve over 88% accuracy after 15 image samples are trained. However, by using combination of these 4 bit-planes based on majority vote, false acceptance rate (FAR) can be reduced from over 80% to 26.2%. False rejection rate (FRR) of the combined approach is 2.2% and half total error rate (HTER) is 14.2%.