TY - GEN
T1 - Multi-Modal Human Authentication Using Silhouettes, Gait and RGB
AU - Guo, Yuxiang
AU - Peng, Cheng
AU - Lau, Chun Pong
AU - Chellappa, Rama
N1 - Funding Information:
VI. ACKNOWLEDGEMENT This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via [2022-21102100005]. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The US. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis. The GaitPattern contributes to robust identification performance over a large range of viewing angles. Extensive experimental results on the CASIA-B dataset demonstrate that the proposed method outperforms state-of-the-art recognition systems. We also provide experimental results using the newly collected BRIAR dataset.
AB - Whole-body-based human authentication is a promising approach for remote biometrics scenarios. Current literature focuses on either body recognition based on RGB images or gait recognition based on body shapes and walking patterns; both have their advantages and drawbacks. In this work, we propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis. The GaitPattern contributes to robust identification performance over a large range of viewing angles. Extensive experimental results on the CASIA-B dataset demonstrate that the proposed method outperforms state-of-the-art recognition systems. We also provide experimental results using the newly collected BRIAR dataset.
UR - http://www.scopus.com/inward/record.url?scp=85149298658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85149298658&partnerID=8YFLogxK
U2 - 10.1109/FG57933.2023.10042572
DO - 10.1109/FG57933.2023.10042572
M3 - Conference contribution
AN - SCOPUS:85149298658
T3 - 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition, FG 2023
BT - 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition, FG 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2023
Y2 - 5 January 2023 through 8 January 2023
ER -