NDLI: Face recognition based on data field

Content Provider	IEEE Xplore Digital Library
Author	Xuejun Cao Zhenyu Wu Jinpeng Chen Ming Zou
Copyright Year	2014
Description	Author affiliation: Comput. Sci. & Eng., BeiHang Univ., Beijing, China (Xuejun Cao; Zhenyu Wu; Jinpeng Chen; Ming Zou)
Abstract	Principal component analysis(PCA) is one of the important methods for extracting the main features of face images. However, it mainly considers the values of the pixels in the images while ignoring the relations between one pixel and other pixels around it. In fact, the pixels of the face images are not independent of each other. Based on this, we propose a new method for extracting the features of face images based on the datafield method which considers the relations between pixels. Furthermore, we combine the data field with PCA(dfPCA) to attract more accurate features from face images. In the experiments, we first analyze the contours of face images with datafiled method. Then, we apply the dfPCA methods on two real world datasets, ORL and yale, for feature extraction respectively. The results show that the recognition rate based on the dfPCA method is better than the PCA method, which demonstrates the feasibility of datafield method for extracting facial features.
Starting Page	496
Ending Page	500
File Size	926368
Page Count	5
File Format	PDF
ISBN	9781479939022
e-ISBN	9781479939039
DOI	10.1109/ICALIP.2014.7009843
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Face recognition feature extraction data field Feature extraction Polynomials Face facerecognition contour Facial features Principal component analysis PCA
Content Type	Text
Resource Type	Article

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