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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning
AU - Baisa, Nathanael L.
AU - Williams, Bryan
AU - Rahmani, Hossein
AU - Angelov, Plamen
AU - Black, Sue
N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2022/6/2
Y1 - 2022/6/2
N2 - In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it is important to extend to consider local information. In this work, we propose hand-based person identification by learning both global and local deep feature representations. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features. For learning the local (part-level) features, we perform uniform partitioning on the conv-layer in both horizontal and vertical directions. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches.
AB - In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it is important to extend to consider local information. In this work, we propose hand-based person identification by learning both global and local deep feature representations. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features. For learning the local (part-level) features, we perform uniform partitioning on the conv-layer in both horizontal and vertical directions. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches.
KW - Deep representation learning
KW - Global features
KW - Hand recognition
KW - Part-level features
KW - Person identification
U2 - 10.1109/IPTA54936.2022.9784133
DO - 10.1109/IPTA54936.2022.9784133
M3 - Conference contribution/Paper
AN - SCOPUS:85133139054
T3 - 2022 11th International Conference on Image Processing Theory, Tools and Applications, IPTA 2022
SP - 1
EP - 6
BT - 2022 11th International Conference on Image Processing Theory, Tools and Applications, IPTA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th International Conference on Image Processing Theory, Tools and Applications, IPTA 2022
Y2 - 19 April 2022 through 22 April 2022
ER -