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 avaijable 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.
face recognition single image per person Iegendre moments image partitioning distance measures order comparator
Rohollah Akbari Mehdi Keshavarz Bahaghighat Javad Mohammadi
Electrical and Computer department Azad University of Qazvin Qazvin, Iran Computer department Raja Umversity of Qazvin Qazvin, Iran Electrical department Azad University of Takestan Takestan, Iran
国际会议
2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)
大连
英文
248-252
2010-07-05（万方平台首次上网日期，不代表论文的发表时间）