Abstract
Automatic face recognition capability in surveillance systems is important for security applications. However, few studies have addressed the problem of outdoor face recognition at a long distance (over 100 meters) in both daytime and nighttime environments. In this paper, we first report on a system that we have designed to collect face image database at a long distance, called the Long Distance Heterogeneous Face Database (LDHF-DB) to advance research on this topic. The LDHF-DB contains face images collected in an outdoor environment at distances of 60 meters, 100 meters, and 150 meters, with both visible light (VIS) face images captured in daytime and near infrared (NIR) face images captured in nighttime. Given this database, we have conducted two types of cross-distance face matching (matching long-distance probe to 1-meter gallery) experiments: (i) intra-spectral (VIS to VIS) face matching, and (ii) cross-spectral (NIR to VIS) face matching. The proposed face recognition algorithm consists of following three major steps: (i) Gaussian filtering to remove high frequency noise, (ii) Scale Invariant Feature Transform (SIFT) in local image regions for feature representation, and (iii) a random subspace method to build discriminant subspaces for face recognition. Experimental results show that the proposed face recognition algorithm outperforms two commercial state-of-the-art face recognition SDKs (FaceVACS and PittPatt) for long distance face recognition in both daytime and nighttime operations. These results highlight the need for better data capture setup and robust face matching algorithms for cross spectral matching at distances greater than 100 meters.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 708-721 |
| Number of pages | 14 |
| Volume | 7725 LNCS |
| Edition | PART 2 |
| DOIs | |
| Publication status | Published - 2013 Apr 11 |
| Event | 11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of Duration: 2012 Nov 5 → 2012 Nov 9 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 7725 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 11th Asian Conference on Computer Vision, ACCV 2012 |
|---|---|
| Country | Korea, Republic of |
| City | Daejeon |
| Period | 12/11/5 → 12/11/9 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Nighttime face recognition at long distance : Cross-distance and cross-spectral matching. / Maeng, Hyunju; Liao, Shengcai; Kang, Dongoh; Lee, Seong Whan; Jain, Anil K.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7725 LNCS PART 2. ed. 2013. p. 708-721 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7725 LNCS, No. PART 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Nighttime face recognition at long distance
T2 - Cross-distance and cross-spectral matching
AU - Maeng, Hyunju
AU - Liao, Shengcai
AU - Kang, Dongoh
AU - Lee, Seong Whan
AU - Jain, Anil K.
PY - 2013/4/11
Y1 - 2013/4/11
N2 - Automatic face recognition capability in surveillance systems is important for security applications. However, few studies have addressed the problem of outdoor face recognition at a long distance (over 100 meters) in both daytime and nighttime environments. In this paper, we first report on a system that we have designed to collect face image database at a long distance, called the Long Distance Heterogeneous Face Database (LDHF-DB) to advance research on this topic. The LDHF-DB contains face images collected in an outdoor environment at distances of 60 meters, 100 meters, and 150 meters, with both visible light (VIS) face images captured in daytime and near infrared (NIR) face images captured in nighttime. Given this database, we have conducted two types of cross-distance face matching (matching long-distance probe to 1-meter gallery) experiments: (i) intra-spectral (VIS to VIS) face matching, and (ii) cross-spectral (NIR to VIS) face matching. The proposed face recognition algorithm consists of following three major steps: (i) Gaussian filtering to remove high frequency noise, (ii) Scale Invariant Feature Transform (SIFT) in local image regions for feature representation, and (iii) a random subspace method to build discriminant subspaces for face recognition. Experimental results show that the proposed face recognition algorithm outperforms two commercial state-of-the-art face recognition SDKs (FaceVACS and PittPatt) for long distance face recognition in both daytime and nighttime operations. These results highlight the need for better data capture setup and robust face matching algorithms for cross spectral matching at distances greater than 100 meters.
AB - Automatic face recognition capability in surveillance systems is important for security applications. However, few studies have addressed the problem of outdoor face recognition at a long distance (over 100 meters) in both daytime and nighttime environments. In this paper, we first report on a system that we have designed to collect face image database at a long distance, called the Long Distance Heterogeneous Face Database (LDHF-DB) to advance research on this topic. The LDHF-DB contains face images collected in an outdoor environment at distances of 60 meters, 100 meters, and 150 meters, with both visible light (VIS) face images captured in daytime and near infrared (NIR) face images captured in nighttime. Given this database, we have conducted two types of cross-distance face matching (matching long-distance probe to 1-meter gallery) experiments: (i) intra-spectral (VIS to VIS) face matching, and (ii) cross-spectral (NIR to VIS) face matching. The proposed face recognition algorithm consists of following three major steps: (i) Gaussian filtering to remove high frequency noise, (ii) Scale Invariant Feature Transform (SIFT) in local image regions for feature representation, and (iii) a random subspace method to build discriminant subspaces for face recognition. Experimental results show that the proposed face recognition algorithm outperforms two commercial state-of-the-art face recognition SDKs (FaceVACS and PittPatt) for long distance face recognition in both daytime and nighttime operations. These results highlight the need for better data capture setup and robust face matching algorithms for cross spectral matching at distances greater than 100 meters.
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UR - http://www.scopus.com/inward/citedby.url?scp=84875911939&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37444-9_55
DO - 10.1007/978-3-642-37444-9_55
M3 - Conference contribution
AN - SCOPUS:84875911939
SN - 9783642374432
VL - 7725 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 708
EP - 721
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -