A modified technique for face recognition under degraded conditions

In this paper an improved face recognition algorithm under degrading conditions is proposed. The proposed algorithm uses a combination of preprocessing techniques coupled with discriminative feature extractors to obtain the best distinctive features for classification. Preprocessing approach is the fusion of multi-scale Weber and enhanced complex wavelet transform. Combination of multiple feature extraction based on Gabor filters, block-based local phase quantization (LPQ) coupled with principal component analysis (PCA) proved to be very effective to improve correct rate of recognition. We have also used two known classifiers, extreme learning machine (ELM), and sparse classifier (SC), and fused their outputs to obtain best recognition rate. Experimental results show improved performance of proposed algorithm under poor illumination, partial occlusion and low-quality images in uncontrolled conditions. Our best recognition results using second version of face recognition grand challenge (FRGC 2.0.4) which is the most challenging database, indicated more than 28% improvement over previous works.