Title: Two-Stream Deep Correlation Network for Frontal Face Recovery
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| Authors: Zhang, T; Dong, QL; Tang, M; Hu, ZY
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| Author Full Names: Zhang, Ting; Dong, Qiulei; Tang, Ming; Hu, Zhanyi
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| Source: IEEE SIGNAL PROCESSING LETTERS, 24 (10):1478-1482; 10.1109/LSP.2017.2736542 OCT 2017
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| Language: English
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| Abstract: Pose and textural variations are two dominant factors to affect the performance of face recognition. It is widely believed that generating the corresponding frontal face froma face image of an arbitrary pose is an effective step toward improving the recognition performance. In the literature, however, the frontal face is generally recovered by only exploring textural characteristic. In this letter, we propose a two-stream deep correlation network, which incorporates both geometric and textural features for frontal face recovery. Given a face image under an arbitrary pose as input, geometric and textural characteristics are first extracted from two separate streams. The extracted characteristics are then fused through the proposed multiplicative patch correlation layer. These two steps are integrated into one network for end-to-end training and prediction, which is demonstrated effective compared with state-of-the-art methods on the benchmark datasets.
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| ISSN: 1070-9908
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| eISSN: 1558-2361
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| IDS Number: FF3DL
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| Unique ID: WOS:000408775600006
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