TY - GEN
T1 - Random sparse representation for thermal to visible face recognition
AU - Reihanian, Samira
AU - Arbabi, Ehsan
AU - Maham, Behrouz
N1 - Funding Information:
1IEEE OTCBVS WS Series Bench; DOE University Research Program in Robotics under grant DOE-DE-FG02-86NE37968; DOD/TACOM/NAC/ARC Program under grant R01-1344-18; FAA/NSSA grant R01-1344-48/49; Office of Naval Research under grant N000143010022.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - Heterogeneous face recognition (HFR) has a prominent importance in sophisticated face recognition systems. Thermal to visible scenario, where the gallery and the probe images are respectively captured in visible and long wavelength infrared (LWIR) band, is one of the most challenging and interesting HFR scenarios. Since the formation of thermal images does not require an external illumination source, the deployment of thermal probe images is practical even in totally darkness conditions such as night security surveillance systems. In this paper, we propose an ensemble classifier which uses the random subspace idea for defining different representations of each image in distinct base learners, and exploits the sparse representation algorithm for the classification of thermal probe images. According to the experimental results, our proposed algorithm leads significant performance improvements in the area of thermal to visible face recognition and achieves the average Rank-1 accuracy of 89.33 percent.
AB - Heterogeneous face recognition (HFR) has a prominent importance in sophisticated face recognition systems. Thermal to visible scenario, where the gallery and the probe images are respectively captured in visible and long wavelength infrared (LWIR) band, is one of the most challenging and interesting HFR scenarios. Since the formation of thermal images does not require an external illumination source, the deployment of thermal probe images is practical even in totally darkness conditions such as night security surveillance systems. In this paper, we propose an ensemble classifier which uses the random subspace idea for defining different representations of each image in distinct base learners, and exploits the sparse representation algorithm for the classification of thermal probe images. According to the experimental results, our proposed algorithm leads significant performance improvements in the area of thermal to visible face recognition and achieves the average Rank-1 accuracy of 89.33 percent.
UR - http://www.scopus.com/inward/record.url?scp=85030558537&partnerID=8YFLogxK
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U2 - 10.1109/ISCC.2017.8024716
DO - 10.1109/ISCC.2017.8024716
M3 - Conference contribution
AN - SCOPUS:85030558537
T3 - Proceedings - IEEE Symposium on Computers and Communications
SP - 1380
EP - 1385
BT - 2017 IEEE Symposium on Computers and Communications, ISCC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium on Computers and Communications, ISCC 2017
Y2 - 3 July 2017 through 7 July 2017
ER -