Real time human face location and recognition system using single training image per person

This paper presents an automatic real time face location and recognition system. The proposed approach detects the face using the combination of hue, saturation and intensity (H SI) and luminance, red chrominance and blue chrominance (Y CrCb) color Space models. The left most, right most and top most pixels of face are detected using threshold values of parameters. One of the eyes is located using the blue chrominance. The second eye, center of the eyes, and the bottom most part of face is detected using geometrical similarity. The face is cropped using these defined boundaries to extract facial region only. The facial features of cropped image are extracted using the combination of Radon and wavelet transform. The technique computes Radon projections in different orientations and captures the directional features of face images. Further the wavelet transform applied on Radon space provides multiresolution features of the facial images. For classification, the nearest neighbor classifier has been used. The performance and robustness of the proposed system is tested on a face database of 785 images of 157 subjects acquired in conditions similar to those encountered in real world applications. The system achieves a recognition rate of 97.8 % and an equal error rate (EER) of about 2.4% for 157 subjects.