TY - GEN
T1 - UHDB11 database for 3D-2D face recognition
AU - Toderici, George
AU - Evangelopoulos, Georgios
AU - Fang, Tianhong
AU - Theoharis, Theoharis
AU - Kakadiaris, Ioannis A.
PY - 2014
Y1 - 2014
N2 - Performance boosts in face recognition have been facilitated by the formation of facial databases, with collection protocols customized to address challenges such as light variability, expressions, pose, sensor/modality differences, and, more recently, uncontrolled acquisition conditions. In this paper, we present database UHDB11, to facilitate 3D-2D face recognition evaluations, where the gallery has been acquired using 3D sensors (3D mesh and texture) and the probes using 2D sensors (images). The database consists of samples from 23 individuals, in the form of 2D high-resolution images spanning six illumination conditions and 12 head-pose variations, and 3D facial mesh and texture. It addresses limitations regarding resolution, variability and type of 3D/2D data and has demonstrated to be statistically more challenging, diverse and information rich than existing cohorts of 10 times larger number of subjects. We propose a set of 3D-2D experimental configurations, with frontal 3D galleries and pose-illumination varying probes and provide baseline performance for identification and verification (available at http://cbl.uh.edu/URxD/ datasets).
AB - Performance boosts in face recognition have been facilitated by the formation of facial databases, with collection protocols customized to address challenges such as light variability, expressions, pose, sensor/modality differences, and, more recently, uncontrolled acquisition conditions. In this paper, we present database UHDB11, to facilitate 3D-2D face recognition evaluations, where the gallery has been acquired using 3D sensors (3D mesh and texture) and the probes using 2D sensors (images). The database consists of samples from 23 individuals, in the form of 2D high-resolution images spanning six illumination conditions and 12 head-pose variations, and 3D facial mesh and texture. It addresses limitations regarding resolution, variability and type of 3D/2D data and has demonstrated to be statistically more challenging, diverse and information rich than existing cohorts of 10 times larger number of subjects. We propose a set of 3D-2D experimental configurations, with frontal 3D galleries and pose-illumination varying probes and provide baseline performance for identification and verification (available at http://cbl.uh.edu/URxD/ datasets).
KW - 3D-2D facial data
KW - computer vision
KW - face databases
KW - face pose
KW - face recognition
KW - identification
KW - illumination
KW - verification
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U2 - 10.1007/978-3-642-53842-1_7
DO - 10.1007/978-3-642-53842-1_7
M3 - Conference contribution
AN - SCOPUS:84958528669
SN - 9783642538414
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 73
EP - 86
BT - Image and Video Technology - 6th Pacific-Rim Symposium, PSIVT 2013, Revised Selected Papers
PB - Springer-Verlag
T2 - 6th Pacific-Rim Symposium on Image and Video Technology, PSIVT 2013
Y2 - 28 October 2013 through 1 November 2013
ER -