Real time face recognition using decision fusion of neural classifiers in the visible and thermal infrared spectrum

This paper is dedicated to multispectral facial image recognition, using decision fusion of neural classifiers. The novelty of this paper is that any classifier is based on the model of Concurrent Self-Organizing Maps (CSOM), previously proposed by first author of this paper. Our main achievement is the implementation of a real time CSOM face recognition system using the decision fusion that combines the recognition scores generated from visual channels {(R, G, and B) or Y} with a thermal infrared classifier. As a source of color and infrared images, we used our VICFACE database of 38 subjects. Any picture has 160 times 120 pixels; for each subject there are pictures corresponding to various face expressions and illuminations, in the visual and infrared spectrum. The spectral sensitivity of infrared images corresponds to the long wave range of 7.5 - 13 mum. The very good experimental results are given regarding recognition score.