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Local Feature Learning using Deep Canonical Correlation Analysis for Heterogeneous Face Recognition
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 Title & Authors
Local Feature Learning using Deep Canonical Correlation Analysis for Heterogeneous Face Recognition
Choi, Yeoreum; Kim, Hyung-Il; Ro, Yong Man;
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Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.
Heterogeneous Face Recognition;Deep Learning;Deep Canonical Correlation Analysis;Local Feature Learning;
 Cited by
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