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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Two-Stream Deep Correlation Network for Frontal Face Recovery
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| 英文论文题目: |
Two-Stream Deep Correlation Network for Frontal Face Recovery
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| 论文题目英文: |
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| 作者: |
Zhang, Ting
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| 论文出处: |
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| 刊物名称: |
IEEE SIGNAL PROCESSING LETTERS
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| 年: |
2017
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| 卷: |
24 |
| 期: |
10 |
| 页: |
1478-1482 |
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| 影响因子: |
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| 摘要: |
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. |
| 英文摘要: |
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