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ACML
2009
Springer

Coupled Metric Learning for Face Recognition with Degraded Images

3 years 10 months ago
Coupled Metric Learning for Face Recognition with Degraded Images
Real-world face recognition systems are sometimes confronted with degraded face images, e.g., low-resolution, blurred, and noisy ones. Traditional two-step methods have limited performance, due to the disadvantageous issues of inconsistent targets between restoration and recognition, over-dependence on normal face images, and high computational complexity. To avoid these limitations, we propose a novel approach using coupled metric learning, without image restoration or any other preprocessing operations. Different from most previous work, our method takes into consideration both the recognition of the degraded test faces as well as the class-wise feature extraction of the normal faces in training set. We formulate the coupled metric learning as an optimization problem and solve it efficiently with a closed-form solution. This method can be generally applied to face recognition problems with various degrade images. Experimental results on various degraded face recognition problems sho...
Bo Li, Hong Chang, Shiguang Shan, Xilin Chen
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ACML
Authors Bo Li, Hong Chang, Shiguang Shan, Xilin Chen
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