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Titolo:
Multi-cues eye detection on gray intensity image
Autore:
Feng, GC; Yuen, PC;
Indirizzi:
Hong Kong Baptist Univ, Dept Comp Sci, Kowloon, Hong Kong, Peoples R ChinaHong Kong Baptist Univ Kowloon Hong Kong Peoples R China Peoples R China Zhongshan Univ, Dept Math, Guangzhou, Peoples R China Zhongshan Univ Guangzhou Peoples R China th, Guangzhou, Peoples R China
Titolo Testata:
PATTERN RECOGNITION
fascicolo: 5, volume: 34, anno: 2001,
pagine: 1033 - 1046
SICI:
0031-3203(200105)34:5<1033:MEDOGI>2.0.ZU;2-I
Fonte:
ISI
Lingua:
ENG
Soggetto:
HUMAN FACE; FRONTAL-VIEW; RECOGNITION; MODEL;
Keywords:
eye detection; face recognition; face detection; eye variance filter;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
22
Recensione:
Indirizzi per estratti:
Indirizzo: Yuen, PC Hong Kong Baptist Univ, Dept Comp Sci, 224 Waterloo Rd, Kowloon, Hong Kong, Peoples R China Hong Kong Baptist Univ 224 Waterloo Rd Kowloon Hong Kong Peoples R China
Citazione:
G.C. Feng e P.C. Yuen, "Multi-cues eye detection on gray intensity image", PATT RECOG, 34(5), 2001, pp. 1033-1046

Abstract

This paper presents a novel eye detection method for gray intensity image. The precise eye position can be located if the eye windows are accurately detected. The proposed method uses multi-cues for detecting eye windows from a face image. Three cues from the face image are used. Each cue indicatesthe positions of the potential eye window. The first cue is the face intensity because the intensity of eye regions is relatively low. The second cueis based on the estimated direction of the line joining the centers of theeyes. The third cue is from the response of convolving the proposed eye variance filter with the face image. Based on the three cues, a cross-validation process is performed. This process generates a list of possible eye window pairs. For each possible case, variance projection function is used foreve detection and verification. A face database from MIT Al laboratory, which contains 930 face images with different orientations and hairstyles captured from different people. is used to evaluate the proposed system. The detection accuracy is 92.5%. (C) 2001 Pattern Recognition Society. Publishedby Elsevier Science Ltd. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 22/01/20 alle ore 12:53:29