关键词: 联络学习, 标架丛, 多流形, 横空间, 纵空间, 单样本训练

Abstract: Small sample size is one challenging problem for face recognition. In many practical applications such as ID card identification, e-passport, even there is only single sample per person. Many traditional methods fail to work in this scenario because there are not enough samples for learning. This paper proposes a novel method which is based on manifold learning to solve this problem. Firstly, this proposed method views local feature (eyes, nose, mouth) of a face as a manifold and uses self-organization mapping neural network to train a multi-manifold structure. Then it associates each manifold by connection operator on frame bundle and learns the directions of inter- manifold and intra-manifold which are not sensitive to the variations of the input. Finally, it adds this additional information to supervised training. The proposed method combines neural network and manifold learning, changing single sample problem to multi-manifold matching problem. Experiments on well-known face databases ORL, UMIST, FERET and AR show that the proposed method outperforms some renowned methods and gets a better performance when facing the problem of variation of expression and pose, etc.

Key words: connection learning, frame bundle, multi-manifold, horizontal space, vertical space, one training sample