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Proceedings Paper

Multiclass classification and recognition method for a Chinese human face
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Paper Abstract

With the development of biometrics technology, the recognition of human-face becomes the most acceptant way of identification. In the recent thirty years, face recognition technology gets more and more attentions. But unfortunately, most human-face recognition systems with a large-scale facial image database can’t be put into practice just because they have not enough recognition speed and precision. As a matter of fact, the recognition time will drastically increase as the number of human-face increases. In order to improve the recognition rates, we can firstly classify the large-scale facial image database into several comparatively small classes with specific criterion, and then begin recognition in the next step. If the classified class is still too big for recognition, another classification could be put into practice with other specific criterion until it adapts to recognition. This method is named as Multi-Layer Classification Method (MLCM) in our paper. In order to classify an unclassified face into a small class, a multiclass classifier must be set up. Because that the mahalanobis distance classifier follows the normal distribution, it is employed in our study. The results have shown that the integrative recognition rates have drastically increased for the large-scale facial image database.

Paper Details

Date Published: 25 August 2004
PDF: 9 pages
Proc. SPIE 5404, Biometric Technology for Human Identification, (25 August 2004); doi: 10.1117/12.541282
Show Author Affiliations
Jiamin Liu, Ministry of Education (China)
Weihong Li, Ministry of Education (China)
Weiguo Gong, Ministry of Education (China)
Yixiong Liang, Ministry of Education (China)
Hongmei Zhang, Ministry of Education (China)


Published in SPIE Proceedings Vol. 5404:
Biometric Technology for Human Identification
Anil K. Jain; Nalini K. Ratha, Editor(s)

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