Face Recognition from a Single Image per Person Using Common Subfaces Method

摘要

In this paper, we propose a face recognition method from a single image per person, called the common subfaces, to solve the "one sample per person" problem. Firstly the single image per person is divided into multiple sub-images, which are regarded as the training samples for feature extraction. Then we propose a novel formulation of common vector analysis from the space isomorphic mapping view for feature extraction. In the procedure of recognition, the common vector of the subfaces from the test face image is derived with the similar procedure to the common vector, which is then compared with the common vector of each class to predict the class label of query face. The experimental results suggest that the proposed common subfaces approach provides a better representation of individual common feature and achieves a higher recognition rate in the face recognition from a single image per person compared with the traditional methods.