Bi‐directional CRC algorithm using CNN‐based features for face classification

Collaborative representation‐based classification (CRC) has become a breakthrough in face classification due to its distinguished collaborative capacity. Nevertheless, insufficient observations of per subject are usually offered by few or even a single gallery image for face classification, which lead to a sensitive response to the variations from the original data set. In this study, the authors present a bi‐directional CRC algorithm using convolutional neural network‐based features for face classification. They first employ a deep convolutional neural network to extract facial features from the original gallery and query sets, and then develop a fast reverse representation model to obtain the auxiliary residual information between each training sample and the reconstructed one that is achieved from the test sample. Secondly, they offer a new solution to the bi‐directional optimisation problem by which the input sample is well represented by the forward linear combination and the reverse one, respectively. The last contribution is to utilise a competitive fusion method for robust face recognition, which weighted reconstructed residuals from the bi‐directional representation model. Experimental results obtained from a set of well‐known face databases including AR, FERET, and ORL verify the validity of the proposed method, especially in the robustness to small sample size problem.