Abstract: Towards the problem that serious affect the recognition rate of face, such as disguise （ e.g. ,scarves, sunglasses and hair） and facial shade by other objects, the face recognition algorithm based on fusion CLAHE and low frequency DCT coefficients transformation was proposed. Firstly, divided the image into several non-overlapping local patches, used the histogram equalization to do local contrast stretching on local sub-block and realized denoising. Secondly, with the appropriate number of low frequency DCT coefficients to eliminate the illumination change in face image. Finally, with kernel principal component analysis, to carry out feature extraction, and with the nearest neighbor classifier, completed the final face recognition. The effectiveness and robustness of the proposed algorithm were experimentally verified in extended Yale B, FRGC V2.0 and an outdoor face database. The experimental results showed the proposed algorithm achieved higher recognition rate when dealing with robust face recognition, compared with several kinds of linear algorithms.

Key words: face recognition, adaptive histogram equalization, low frequency discrete cosine transform, coefficients retransform