基于三元组损失函数的深度人脸哈希方法

大规模人脸数据集上的快速检索是人脸识别应用的关键问题。较短长度人脸哈希方法可降低人脸特征比对的计算量，有助于大规模人脸识别的应用。为此提出了一种基于三元组损失函数的深度人脸哈希方法，通过优化三元组损失函数训练深度卷积神经网络以提取图像深层特征，使得由该特征表征的同类图像在欧式空间中的距离尽可能小，不同类图像在欧式空间中的距离尽可能大；通过在深度网络后添加随机映射层，进一步将高维特征映射到低维空间，并通过阈值化将低维空间映射到汉明空间。在多个标准的数据集上的实验结果表明本文方法相比于现有其他方法的优越性。

Deep face Hashing based on ternary-group loss function

Fast retrieval on large scale human face data sets is a key problem in face recognition applications. The face Hashing method with short length can reduce the computational amount of face feature alignment, and is helpful to the application of large-scale face recognition. In this paper, a deep face Hashing method based on the loss function of ternary-group is presented. By optimizing the loss function of ternary-group, the deep convolution neural network is trained to extract the deep feature of images. The distance between the similar images can be as small as possible, and that between different kinds of images is as large as possible. The high dimension feature is mapped to the low dimension space by adding the random mapping layer following the deep network. And the low dimension space is further mapped to Hamming space by the threshold algorithm. Experimental results on multiple standard datasets show that the proposed method outperforms other state-of-the-art methods.