Complete discriminative feature learning: A new approach for heterogeneous face recognition
Yi Jin, Jiwen Lu, Qiuqi Ruan, Yap‐Peng Tan · 2014
In this paper, we propose a new feature learning approach called complete discriminative feature learning (CDFL) for heterogeneous face recognition. Unlike most existing heterogeneous face recognition methods where hand-crafted feature descriptors are used for face representation, the proposed CD-FL aims to learn an optimal weighted discriminative image filter to improve learning discriminative filters, so that complete discriminative information is exploited and the feature difference between different modalities is effectively reduced, simultaneously. Experimental results shows that our approach consistently outperforms the state-of-the-art methods.