An effective method to tackle illumination problem in collaborative representation based classification
Mohammed Ahmed, Magzoub Albashier, Alan W.C. Tan, Azlan Abdaziz, Fawaz Sammani · 2017
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 L1norm minimization. A newer Approach called Collaborative representation based classification uses the same way as Sparse representative, but it recovers the solution using L2norm 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.