Domain adaptive sparse representation-based classification

In recent years, sparse representation and dictionary learning methods have produced state-of-the-art results in many biometric recognition problems such as face, gait and iris recognition. However, when sparse representation-based classification methods are confronted with situations where the training data has different distribution than the test data, their performance degrades significantly. In this paper, we propose a general sparse representation-based classification method that learns projections of data in a space where the sparsity of data is maintained.

We propose an efficient iterative procedure for solving the proposed optimization problem. One of the key features of the proposed method is that it is computationally efficient as the learning is done in the lower-dimensional space. Various experiments on mobile active authentication datasets consisting of face and screen touch gestures show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive domain adaptation algorithms.

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