Illumination invariant face recognition using near-infrared images

Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel soluti...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 29(2007), 4 vom: 13. Apr., Seite 627-39
1. Verfasser: Li, Stan Z (VerfasserIn)
Weitere Verfasser: Chu, Rufeng, Liao, Shengcai, Zhang, Lun
Format: Aufsatz
Sprache:English
Veröffentlicht: 2007
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Evaluation Study Journal Article Research Support, Non-U.S. Gov't
LEADER 01000naa a22002652 4500
001 NLM168403161
003 DE-627
005 20231223115032.0
007 tu
008 231223s2007 xx ||||| 00| ||eng c
028 5 2 |a pubmed24n0561.xml 
035 |a (DE-627)NLM168403161 
035 |a (NLM)17299220 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Li, Stan Z  |e verfasserin  |4 aut 
245 1 0 |a Illumination invariant face recognition using near-infrared images 
264 1 |c 2007 
336 |a Text  |b txt  |2 rdacontent 
337 |a ohne Hilfsmittel zu benutzen  |b n  |2 rdamedia 
338 |a Band  |b nc  |2 rdacarrier 
500 |a Date Completed 24.04.2007 
500 |a Date Revised 10.12.2019 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups 
650 4 |a Evaluation Study 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Chu, Rufeng  |e verfasserin  |4 aut 
700 1 |a Liao, Shengcai  |e verfasserin  |4 aut 
700 1 |a Zhang, Lun  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on pattern analysis and machine intelligence  |d 1979  |g 29(2007), 4 vom: 13. Apr., Seite 627-39  |w (DE-627)NLM098212257  |x 1939-3539  |7 nnns 
773 1 8 |g volume:29  |g year:2007  |g number:4  |g day:13  |g month:04  |g pages:627-39 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 29  |j 2007  |e 4  |b 13  |c 04  |h 627-39