Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person

With the aid of a universal facial variation dictionary, sparse representation based classifier (SRC) has been naturally extended for face recognition (FR) with single sample per person (SSPP) and achieved promising performance. However, extracting discriminative facial features and building powerful representation framework for classifying query face images are still the bottlenecks of improving the performance of FR with SSPP. In this paper, by densely sampling and sparsely detecting facial points, we extract complete and robust local regions and learn convolution features adaptive to the local regions and discriminative to the face identity by using convolutional neural networks (CNN). With this powerful facial description and a generic face dataset with common facial variations, a joint and collaborative representation framework, which performs representation for each local region of the query face image while requires all regions of the query face image to have similar representation coefficients, is presented to exploit the distinctiveness and commonality of different local regions. In the proposed joint and collaborative representation with local adaptive convolution feature (JCRACF), both discriminative local facial features that are robust to various facial variations and powerful representation dictionaries of facial variations that can overcome the small-sample-size problem are fully exploited. JCR-ACF has been extensively evaluated on several popular databases including AR, CMU Multi-PIE, LFW and the large-scale CASIA-WebFace databases. Experimental results demonstrate the much higher robustness and effectiveness of JCR-ACF to complex facial variations compared to the state-of-the-art methods.