Learn to Recognise Exploring Priors of Sparse Face Recognition on Smartphones
Abstract: Face recognition is one of the important components of many smart devices apps, e.g., face unlocking, people tagging and games on smart phones, tablets, or smart glasses. Sparse Representation Classification (SRC) is a state state-of of-the-art face recognition algorithm, orithm, which has been shown to outperform many classical face recognition algorithms in OpenCV, e.g., Eigenface algorithm. The success of SRC is due to its use of ℓ1 optimization, which makes SRC robust to noise and occlusions. Since ℓ1 optimization is co computationally mputationally intensive, SRC uses random projection matrices to reduce the dimension of the ℓ1 problem. However, random projection matrices do not give consistent classification accuracy as they ignored the prior knowledge of the training set. In this pape paper, r, we propose to exploit the prior knowlege of the training set to improve the recognition accuracy. It first learns the optimized projection matrix from the training set to produce consistent recognition performance then applies ℓ1 -based classification based ased on the group sparsity structure of SRC to further improve the recognition accuracy. Our evaluations, based on publicly available databases and real experiment, show that face recognition using optimized projection matrix is 88-17 17 percent more accurate than its random counterpart and Eigenface algorithm, and the recognition accuracy can be further improved by up to 5 percent by exploiting group sparsity structure. Furthermore, the optimized projection matrix does not have to be rere calculated even if new faces are added to the training set. We implement the SRC