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Joint Multi-Patch and Multi-Task CNNs for Robust Face Recognition
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2020/10/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Pattern Recognition
CNN, multi-task learning, face representation learning, face recognition,
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In this paper, we present a joint multi-patch and multi-task convolutional neural networks (JMM-CNNs) framework to learn more descriptive and robust face representation for face recognition. In the proposed JMM-CNNs, a set of multi-patch CNNs and a feature fusion network are constructed to learn and fuse global and local facial features, then a multi-task learning algorithm, including face recognition task and pose estimation task, is operated on the fused feature to obtain a pose-invariant face representation for the face recognition task. To further enhance the pose insensitiveness of the learned face representation, we also introduce a similarity regularization term on features of the two tasks to propose a regularization loss. Moreover, a simple but effective patch sampling strategy is applied to make the JMM-CNNs have an end-to-end network architecture. Experiments on Multi-PIE dataset demonstrate the effectiveness of the proposed method, and we achieve a competitive performance compared with state-of-the-art methods on Labeled Face in the Wild (LFW), YouTube Faces (YTF) and MegaFace Challenge.