Multi-view face recognition using deep neural networks

F Face recognition has been widely used in modern intelligent systems, such as smart video surveillance, online payment, and intelligent access control system. Existing face recognition algorithms are prone to be attacked by various face presentation attacks (face-PAs), such as printed paper, video replay, and silicone masks. To optimally handle the aforementioned problems, we formulate a novel deep architecture to increase the accuracy of multi-view human face recognition. In particular, in the first place, a novel deep neural network is built for deeply encoding the face regions, where a novel face alignment algorithm is employed to localize the key points inside faces. Subsequently, we utilize the well-known PCA for reducing the dimensionality of the deep features and simultaneously, removing the redundant and contaminated visual features. Thereafter, we propose a joint Bayesian framework in order to evaluate the similarity of feature vectors and highly competitive face classification accuracy can be achieved. Comprehensive experiments were conducted on our compiled CAS-PEAL dataset and achieved a 98.52% face recognition performance. Moreover, our proposed face recognition system can robustly handle various face recognition attack under various contexts. (C) 2020 Elsevier B.V. All rights reserved.