Legendre moments for face identification based on single image per person

One of the main challenges faced by the current face recognition techniques lies in the difficulties of collecting samples. Fewer samples per person mean less laborious effort for collecting them, lower cost for storing and processing them. Unfortunately, many reported face recognition techniques rely heavily on the size and representative of training set, and most of them will suffer serious performance drop or even fail to work if only one training sample per person is available to the systems. In this paper, a recognition algorithm based on feature vectors of Legendre moments is introduced as an attempt to solve the single image problem. Subset of 200 images from FERET database and 100 images from AR database are used in our experiments. The results reported in this paper show that the proposed method achieves 91% and 89.5% accuracy for AR and FERET, respectively.