Orthonormal Product Quantization Network for Scalable Face Image Retrieval
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Recently, deep hashing with Hamming distance metric has drawn increasing attention for face image retrieval tasks. However, its counterpart deep quantization methods, which learn binary code representations with dictionary-related distance metrics, have seldom been explored for the task. This paper makes the first attempt to integrate product quantization into an end-to-end deep learning framework for face image retrieval. Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme using predefined orthonormal vectors as codewords, which aims to enhance the quantization informativeness and reduce the codewords' redundancy. To make the most of the discriminative information, we design a tailored loss function that maximizes the identity discriminability in each quantization subspace for both the quantized and the original features. Furthermore, an entropy-based regularization term is imposed to reduce the quantization error. We conduct experiments on three commonly-used datasets under the settings of both single-domain and cross-domain retrieval. It shows that the proposed method outperforms all the compared deep hashing/quantization methods under both settings with significant superiority. The proposed codewords scheme consistently improves both regular model performance and model generalization ability, verifying the importance of codewords' distribution for the quantization quality. Besides, our model's better generalization ability than deep hashing models indicates that it is more suitable for scalable face image retrieval tasks.
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