TY - GEN
T1 - Image quality assessment to enhance infrared face recognition
AU - Pulecio, Camilo G.Rodriguez
AU - Benitez-Restrepo, Hernan D.
AU - Bovik, Alan C.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Automatic quality evaluation of infrared images has not been researched as extensively as for images of the visible spectrum. Moreover, there is a lack of studies on the influence of degradation of image quality on the performance of computer vision tasks operating on thermal images. Here, we quantify the impact of common image distortions on infrared face recognition, and present a method for aggregating perceptual quality-aware features to improve the identification rates. We use Natural Scene Statistics (NSS) to detect degradation of infrared images, and to adapt the face recognition algorithm to the quality of the test image. The proposed approach applied to a face identification algorithm based on thermal signatures yielded an improvement of rank one recognition rates between 11% and 19%. These results confirm the relevance of image quality assessment for improving biometric identification systems that use thermal images.
AB - Automatic quality evaluation of infrared images has not been researched as extensively as for images of the visible spectrum. Moreover, there is a lack of studies on the influence of degradation of image quality on the performance of computer vision tasks operating on thermal images. Here, we quantify the impact of common image distortions on infrared face recognition, and present a method for aggregating perceptual quality-aware features to improve the identification rates. We use Natural Scene Statistics (NSS) to detect degradation of infrared images, and to adapt the face recognition algorithm to the quality of the test image. The proposed approach applied to a face identification algorithm based on thermal signatures yielded an improvement of rank one recognition rates between 11% and 19%. These results confirm the relevance of image quality assessment for improving biometric identification systems that use thermal images.
KW - Biometrics
KW - Face Recognition
KW - Image Quality
KW - Infrared Imaging
KW - Natural Scene Statistics
UR - http://www.scopus.com/inward/record.url?scp=85045335391&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2017.8296392
DO - 10.1109/ICIP.2017.8296392
M3 - Conference contribution
AN - SCOPUS:85045335391
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 805
EP - 809
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -