Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization
American Journal of Software Engineering and Applications
Volume 4, Issue 3, June 2015, Pages: 50-55
Received: Apr. 21, 2015;
Accepted: May 1, 2015;
Published: May 13, 2015
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Yang Hongli, Science College, Shandong University of Science and Technology, Qingdao, Shandong, P. R. China
Hu Yunhong, Applied Mathematics Department, Yuncheng University, Yuncheng, P. R. China
Data missing usually happens in the process of data collection, transmission, processing, preservation and application due to various reasons. In the research of face recognition, the missing of image pixel value will affect feature extraction. How to extract local feature from the incomplete data is an interesting as well as important problem. Nonnegative matrix factorization (NMF) is a low rank factorization method for matrix and has been successfully used in local feature extraction in various disciplines which face recognition is included. This paper mainly deals with this problem. Firstly, we classify the patterns of image pixel value missing, secondly, we provide the local feature extraction models basing on nonnegative matrix factorization under different types of missing data, thirdly, we compare the local feature extraction capabilities of the above given models under different missing ratio of the original data. Recognition rate is investigated under different data missing pattern. Numerical experiments are presented and conclusions are drawn at the end of the paper.
Local Feature Extraction Models from Incomplete Data in Face Recognition Based on Nonnegative Matrix Factorization, American Journal of Software Engineering and Applications.
Vol. 4, No. 3,
2015, pp. 50-55.
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