An effective method to tackle illumination problem in collaborative representation based classification

Many algorithms for face recognition have been used in researches. Sparse representation based classification is an approach that classifies a sample with over complete dictionary. The testing can be recovered via L
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norm minimization. A newer Approach called Collaborative representation based classification uses the same way as Sparse representative, but it recovers the solution using L
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norm minimization. Both collaborative representation and sparse representation deal with only a small variation in pose and illumination. In this paper, we propose an approach to tackle the problem of illumination variation in collaborative representation. Our method is a combination between collaborative representation and logarithmic total variation (LTV). In this approach we are using LTV as a pre-processing step to our algorithm. LTV has made a huge impact on the result.