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Titolo:
A new neuro-fuzzy classifier with application to on-line face detection and recognition
Autore:
Taur, JS; Tao, CW;
Indirizzi:
Natl Chung Hsing Univ, Dept Elect Engn, Taichung 40227, Taiwan Natl Chung Hsing Univ Taichung Taiwan 40227 Engn, Taichung 40227, Taiwan Natl Ilan Inst Technol, Dept Elect Engn, Ilan, Taiwan Natl Ilan Inst Technol Ilan Taiwan chnol, Dept Elect Engn, Ilan, Taiwan
Titolo Testata:
JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
fascicolo: 3, volume: 26, anno: 2000,
pagine: 397 - 409
SICI:
1387-5485(200011)26:3<397:ANNCWA>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
NETWORK;
Keywords:
neuro-fuzzy classifier; pattern recognition; face detection; face recognition;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
16
Recensione:
Indirizzi per estratti:
Indirizzo: Taur, JS Natl Chung Hsing Univ, Dept Elect Engn, Taichung 40227, Taiwan Natl Chung Hsing Univ Taichung Taiwan 40227 chung 40227, Taiwan
Citazione:
J.S. Taur e C.W. Tao, "A new neuro-fuzzy classifier with application to on-line face detection and recognition", J VLSI S P, 26(3), 2000, pp. 397-409

Abstract

In this paper, we propose a neuro-fuzzy classifier (NEFCAR) that utilizes positive and negative rules with different rule importances to create the decision boundaries between different classes. The locally unsupervised and globally supervised training technique is adopted. The decision-based and approximation-based strategies are combined to provide a suitable amount of training for each training pattern. The reinforced and anti-reinforced learning rules are given with different weighting so that the training can be efficient and can reach convergence quickly. Moreover, NEFCAR can easily provide the confidence measure of each classification decision. Therefore, therejection algorithm can be implemented in a straightforward manner. Noise tolerant training is conducted to improve the generalization performance and the confidence measure is adopted to avoid overtraining. The proposed classifier is applied to two applications. The first one is the Fisher iris data classification, and the second one is an on-line face detection and recognition application. Good classification results are obtained in both applications. In the on-line face detection and recognition system, two NEFCAR'sare utilized: a two-class and a multi-class NEFCAR's are adopted to detectthe face and recognize the face, respectively. The color of skin and the motion information are taken into consideration heuristically to improve theeffectiveness of the face location algorithm.

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Documento generato il 20/01/20 alle ore 11:09:53