TY - GEN
T1 - Deep Convolutional Feature-based Gait Recognition Using Silhouettes and RGB Images
AU - Içik, Selin Gök
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Today, many different biometrie features are used for human identification. Unlike biometrie features, such as eye, iris, ear, and fingerprint, gait biometrics enables recognition from long distance and low resolution images. In this paper, different design choices for a deep learning-based gait recognition system are investigated in detail. Some preprocessing steps, such as human silhouette extraction and gait cycle calculation are eliminated to make the system suitable for practical applications. To assess different input types' effect on the gait recognition performance, both binary silhouettes and RGB images are given as input to the network. To observe the contribution of transfer learning, we fine-tuned a pre-trained generic object recognition model with the CASIA-B gait dataset and performed experiments on the OU-ISIR Large Population gait dataset. To observe the effect of pose variations, we conducted experiments for both identical-view and cross-view conditions. Successful results are obtained, especially for cross-view gait recognition, compared to different approaches for gait recognition.
AB - Today, many different biometrie features are used for human identification. Unlike biometrie features, such as eye, iris, ear, and fingerprint, gait biometrics enables recognition from long distance and low resolution images. In this paper, different design choices for a deep learning-based gait recognition system are investigated in detail. Some preprocessing steps, such as human silhouette extraction and gait cycle calculation are eliminated to make the system suitable for practical applications. To assess different input types' effect on the gait recognition performance, both binary silhouettes and RGB images are given as input to the network. To observe the contribution of transfer learning, we fine-tuned a pre-trained generic object recognition model with the CASIA-B gait dataset and performed experiments on the OU-ISIR Large Population gait dataset. To observe the effect of pose variations, we conducted experiments for both identical-view and cross-view conditions. Successful results are obtained, especially for cross-view gait recognition, compared to different approaches for gait recognition.
KW - Biometrie
KW - Cross-view
KW - Deep learning
KW - Gait recognition
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85125863747&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125863747&partnerID=8YFLogxK
U2 - 10.1109/UBMK52708.2021.9559026
DO - 10.1109/UBMK52708.2021.9559026
M3 - Conference contribution
AN - SCOPUS:85125863747
T3 - Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
SP - 336
EP - 341
BT - Proceedings - 6th International Conference on Computer Science and Engineering, UBMK 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Computer Science and Engineering, UBMK 2021
Y2 - 15 September 2021 through 17 September 2021
ER -