NDLI: A reliable face recognition algorithm based on matching normalized gradients

Content Provider	IEEE Xplore Digital Library
Author	Chyuan-Huei Thomas Yang Shang-Hong Lai Long-Wen Chang
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan (Chyuan-Huei Thomas Yang; Shang-Hong Lai; Long-Wen Chang)
Abstract	Face recognition is a challenging problem that has attracted a number of researchers from academia and industry in recent years. One of the main challenges in face image recognition is due to illumination variations. Face matching is a crucial step for these problems. A reliable face recognition algorithm robust against illumination variation is proposed. The basic idea in our method is to accumulate the consistency measure of corresponding normalized gradients at the face contour locations from two comparing face images. The consistency measure is defined to be the inner product between two normalized gradient vectors at the corresponding locations in the two images. To compensate for lighting variations, three face images captured with very different lighting directions of each person are integrated into our face recognition method. We tested the proposed algorithm on the Yale face database B and compared with three other methods. Satisfactory recognition results are shown from face recognition experiments under different illumination conditions.
Sponsorship	New Jersey Inst. of Technol. Jersy Comm. on Higher Educ. sponsored by NJ I-TOWER program at NJIT IEEE North Jersy Section IEEE COMSOC North Jersey Chapter
Starting Page	559
Ending Page	562
File Size	324684
Page Count	4
File Format	PDF
ISBN	0780377249
DOI	10.1109/ITRE.2003.1270681
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-08-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Lighting Robustness Image matching Image recognition Feature extraction Computer science Computer industry Testing Image databases
Content Type	Text
Resource Type	Article

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