Kernel group sparse representation based classifier for multimodal biometrics

Classification is an important pattern recognition paradigm with a multitude of applications in popular research problems. Utilizing multiple data representations to improve the accuracy of classification has been explored in literature. However, approaches such as combining classifiers using majority voting and score level fusion do not utilize the underlying structure of the data which is available at the representation stage itself. In this paper, we propose a kernelization based extension to the group sparse representation classifier which can utilize multiple representations of input data to improve classification performance. By using a kernel, these representations are processed in a higher dimensional space where they are more separable, without substantially increasing computational costs. The proposed algorithm selects the ideal kernel to use along with its parameters automatically as part of the training process. We evaluate the proposed algorithm on three challenging biometric problems namely, cross distance face recognition, RGB-D face recognition, and multimodal biometrics to showcase its efficacy. Experimentally, we observe that the proposed algorithm can efficiently combine multiple data representations to further improve classification performance.