A method is proposed, which combines adaptive histogram equalization (AHE), Gabor wavelet and LTP, to improve the video-based facial recognition under left, right, up, down and front illumination. Firstly, the AHE is used to reduce illumination variations on the existed face images from YaleB and CMU PIE face databases. Then, the images are convolved with Gabor filters to extract their corresponding Gabor feature maps and the LTP is used on each Gabor feature map to extract the local neighbor pattern. Finally, the input face image is described by using the histogram sequence extracted from all these region patterns. The results compared with the published results on YaleB and CMU PIE face databases of changing illumination verified the validity of the proposed method.
; Hua-Feng Wang
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