TY - GEN
T1 - Recursive binary template embedding for face image sets
AU - Leng, Mengjun
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
studies that the proposed recursive coding structure, the angular similarity, and the loss level fusion contribute together to the final improvements. Acknowledgement: This material is based upon work supported in part by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project Image and Video Person Identification in an Operational Environment: Phase I awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In this paper, we focus on the task of generating compact binary templates for face image sets. Existing set-based templates are extracted from a high-dimensional real-value feature space, and sometimes with complex structure. The process is not very efficient for large-scale applications. Existing binary templates are generated via either a sequence or a tree of projections. The recursive tree structure achieves higher recognition performance, but the number of projections increases exponentially with the binary code length. Thus, we propose a recursive binary embedding algorithm exhibiting an increased recognition power while restricting the number of projections to increase linearly with the code length. Moreover, the proposed embedding can be easily plugged into the modern deep learning architectures for set-based face recognition. Experimental results using the challenge IJB-A dataset illustrate the effectiveness and efficiency of our proposed template for face verification, closed set identification, and open set identification.
AB - In this paper, we focus on the task of generating compact binary templates for face image sets. Existing set-based templates are extracted from a high-dimensional real-value feature space, and sometimes with complex structure. The process is not very efficient for large-scale applications. Existing binary templates are generated via either a sequence or a tree of projections. The recursive tree structure achieves higher recognition performance, but the number of projections increases exponentially with the binary code length. Thus, we propose a recursive binary embedding algorithm exhibiting an increased recognition power while restricting the number of projections to increase linearly with the code length. Moreover, the proposed embedding can be easily plugged into the modern deep learning architectures for set-based face recognition. Experimental results using the challenge IJB-A dataset illustrate the effectiveness and efficiency of our proposed template for face verification, closed set identification, and open set identification.
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U2 - 10.1109/BTAS.2018.8698595
DO - 10.1109/BTAS.2018.8698595
M3 - Conference contribution
AN - SCOPUS:85065412585
T3 - 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
BT - 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Biometrics Theory, Applications and Systems, BTAS 2018
Y2 - 22 October 2018 through 25 October 2018
ER -