Supervised trace lasso for robust face recognition

Jian Lai, Xudong Jiang

Abstract: In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters the highly correlated data together. However, the face images with similar variations, such as illumination or expression, often have higher correlation than those from the same class. In this case, the result of trace lasso is contradictory to the goal of recognition, which is to cluster the samples according to their identities. Therefore, trace lasso is not a good choice for face recognition task. In this work, we propose a supervised trace lasso (STL) framework by employing the class label information. To represent the query sample, the proposed STL approach seeks the sparsity of the number of classes instead of the number of training samples. This directly coincides with the objective of the classification. Furthermore, an efficient algorithm to solve the optimization problem of proposed method is given. The extensive experimental results have demonstrated the effectiveness of the proposed framework.