科研之友
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摘要
Existing deep quantization methods provided an efficient solution for large-scale image retrieval. How-ever, the significant intra-class variations, like pose, illumination, and expressions in face images, still pose a challenge. In light of this, face image retrieval requires sufficiently powerful learning metrics, which are absent in current deep quantization works. Moreover, to tackle the growing unseen identi-ties in the query stage, face image retrieval drives more demands regarding model generalization and scalability than general image retrieval tasks. This paper integrates product quantization with orthonor-mal constraints into an end-to-end deep learning framework to effectively retrieve face images. Specif-ically, we propose a novel scheme that uses predefined orthonormal vectors as codewords to enhance the quantization informativeness and reduce codewords' redundancy. A tailored loss function maximizes discriminability among identities in each quantization subspace for both the quantized and original fea-tures. An entropy-based regularization term is imposed to reduce the quantization error. Experiments are conducted on four commonly-used face datasets under both seen and unseen identity retrieval settings. Our method outperforms all the compared state-of-the-art under both settings. The proposed orthonor-mal codewords consistently boost both models' standard retrieval performance and generalization ability, demonstrating the superiority of our method for scalable face image retrieval.
