关键词: 人脸识别, 自归一化神经网络, 耦合映射, 子空间, 类内距, 类间距

Abstract:

Traditional face recognition algorithms tend to decline when it occurs to low resolution face images. In order to solve this problem, a Light Discriminative Self-normalizing Neural Network（LDSNN） model is proposed, which extracts discriminative features from High-Resolution（HR） images and corresponding Low-Resolution（LR） images, and learns a coupled mapping which transforms the features to a common subspace. While the property of self-normalizing resulting from scaled exponential linear units accelerates the training stage. A loss function is designed to minimize intraclass distances and enlarge interclass distances based on not only the discriminability of HR-LR features, but also the similarity between them. Thus features from the same subject are more compacted together. The recognition rate of the proposed LDSNN model on a standard and two surveillance databases are 95.57%, 94.10%, and 84.56% respectively, better than other algorithms, which demonstrates that the proposed method works well with uncontrolled low-resolution face recognition.

Key words: face recognition, self-normalizing neural network, coupled mapping, sub-space, intraclass distances, interclass distances