With the advantages of low storage cost and extremely fast retrieval speed, deep hashing methods have attracted much attention for image retrieval recently. However, large-scale face image retrieval with significant intra-class variations is still challenging. Neither existing pairwise/triplet labels-based nor softmax classification loss-based deep hashing works can generate compact and discriminative binary codes. Considering these issues, we propose a center-based framework integrating end-to-end hashing learning and class centers learning simultaneously. The framework minimizes the intra-class variance by clustering intra-class samples into a learnable class center. To strengthen inter-class separability, it additionally imposes a novel regularization term to enlarge the Hamming distance between pairwise class centers. Moreover, a simple yet effective regression matrix is introduced to encourage intra-class samples to generate the same binary codes, which further enhances the hashing codes compactness. Experiments on four large-scale datasets show the proposed method outperforms state-of-the-art baselines under various code lengths and commonly-used evaluation metrics.

具有低存储成本和极快的检索速度的优点，深度散列方法最近引起了图像检索的广泛关注。然而，具有明显的类内差异的大规模面部图像检索仍然具有挑战性。现有的基于成对/三元组标签和基于softmax分类损失的深度哈希工作都无法生成紧凑而有区别的二进制代码。考虑到这些问题，我们提出了一个基于中心的框架，该框架同时集成了端到端哈希学习和类中心学习。该框架通过将类内样本聚集到一个可学习的类中心中来最大程度地减少类内差异。为了加强类之间的可分离性，它还附加了一个新颖的正则化项，以扩大成对类中心之间的汉明距离。而且，引入了一个简单而有效的回归矩阵来鼓励类内样本生成相同的二进制代码，从而进一步提高了哈希码的紧凑性。在四个大型数据集上进行的实验表明，在各种代码长度和常用评估指标下，该方法的性能均优于最新的基准。