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Face recognition using line edge map (2002)
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| Venue: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
| Citations: | 104 - 13 self |
BibTeX
@ARTICLE{Gao02facerecognition,
author = {Yongsheng Gao and Maylor K. H. Leung},
title = {Face recognition using line edge map},
journal = {IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE},
year = {2002},
volume = {24},
pages = {764--779}
}
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Abstract
The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face." Furthermore, lighting condition changes, facial expressions, and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposed a novel concept, "faces can be recognized using line edge map." A compact face feature, Line Edge Map (LEM), is generated for face coding and recognition. A thorough investigation on the proposed concept is conducted which covers all aspects on human face recognition, i.e., face recognition, under 1) controlled/ideal condition and size variation, 2) varying lighting condition, 3) varying facial expression, and 4) varying pose. The system performances are also compared with the eigenface method, one of the best face recognition techniques, and reported experimental results of other methods. A face prefiltering technique is proposed to speed up the searching process. It is a very encouraging finding that the proposed face recognition technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that LEM together with the proposed generic line segment Hausdorff distance measure provide a new way for face coding and recognition.
Keyphrases
line edge map face recognition facial expression face coding eigenface method face recognition technique size variation human face recognition minor difference compact face feature thorough investigation human face significant challenge pattern analysis pattern recognition research community searching process encouraging finding system performance condition change difficult problem comparison experiment ideal condition face prefiltering technique new way automatic recognition face recognition task novel concept experimental result
