Abstract
Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.
| Original language | English |
|---|---|
| Title of host publication | Pattern Recognition |
| Publisher | Elsevier Ltd |
| Pages | 3750-3766 |
| Number of pages | 17 |
| Volume | 47 |
| Edition | 12 |
| DOIs | |
| Publication status | Published - 2014 Dec 1 |
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Keywords
- Cross-distance matching
- Cross-spectral matching
- Heterogeneous face matching
- Image restoration
- K-means clustering
- Locally Linear Embedding (LLE)
- Nighttime face recognition
ASJC Scopus subject areas
- Software
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Nighttime face recognition at large standoff : Cross-distance and cross-spectral matching. / Kang, Dongoh; Han, Hu; Jain, Anil K.; Lee, Seong Whan.
Pattern Recognition. Vol. 47 12. ed. Elsevier Ltd, 2014. p. 3750-3766.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Nighttime face recognition at large standoff
T2 - Cross-distance and cross-spectral matching
AU - Kang, Dongoh
AU - Han, Hu
AU - Jain, Anil K.
AU - Lee, Seong Whan
PY - 2014/12/1
Y1 - 2014/12/1
N2 - Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.
AB - Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.
KW - Cross-distance matching
KW - Cross-spectral matching
KW - Heterogeneous face matching
KW - Image restoration
KW - K-means clustering
KW - Locally Linear Embedding (LLE)
KW - Nighttime face recognition
UR - http://www.scopus.com/inward/record.url?scp=84907712027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907712027&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2014.06.004
DO - 10.1016/j.patcog.2014.06.004
M3 - Chapter
AN - SCOPUS:84907712027
VL - 47
SP - 3750
EP - 3766
BT - Pattern Recognition
PB - Elsevier Ltd
ER -