Residential College | false |
Status | 已發表Published |
CO3Net: Coordinate-Aware Contrastive Competitive Neural Network for Palmprint Recognition | |
Yang,Ziyuan1; Xia,Wenjun1; Qiao,Yifan1; Lu,Zexin1; Zhang,Bob2; Leng,Lu3; Zhang,Yi4 | |
2023 | |
Source Publication | IEEE Transactions on Instrumentation and Measurement |
ISSN | 0018-9456 |
Volume | 72 |
Abstract | Palmprint recognition achieves high discrimination for identity verification. Compared with handcrafted local texture descriptors, convolutional neural networks (CNNs) can spontaneously learn optimal discriminative features without any prior knowledge. To further enhance the features' representation and discrimination, we propose a coordinate-aware contrastive competitive neural network (CO3Net) for palmprint recognition. To extract the multiscale textures, CO3Net consists of three parallel learnable Gabor filters (LGFs)-based texture extraction branches that learn the discriminative and robust ordering features. Due to the heterogeneity of palmprints, the effects of different textures on the final recognition performance are inconsistent, and dynamically focusing on the textures is beneficial to the performance improvement. Then, CO3Net introduces the attention modules to explore the spatial information, and selects more robust and discriminative textures. Specifically, coordinate attention (CA) is embedded into CO3Net to adaptively focus on the important textures from the positional information. Since it is difficult for the cross-entropy loss to build a compact intraclass and separate interclass feature space, the contrastive loss is employed to jointly optimize the network. CO3Net is validated on four public datasets, and the results demonstrate the remarkable recognition performance of the proposed CO3Net compared to the other state-of-the-art methods. |
Keyword | Contrastive Learning Coordinate Attention (Ca) Deep Learning (Dl) Gabor Filter Palmprint Recognition |
DOI | 10.1109/TIM.2023.3276506 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Engineering ; Instruments & Instrumentation |
WOS Subject | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS ID | WOS:000996515600022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Scopus ID | 2-s2.0-85161296627 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Corresponding Author | Zhang,Yi |
Affiliation | 1.Sichuan University,College of Computer Science,Chengdu,610065,China 2.University of Macau,Pattern Analysis and Machine Intelligence Group,Department of Computer and Information Science,Macao 3.Nanchang Hangkong University,School of Software,Nanchang,330063,China 4.Sichuan University,School of Cyber Science and Engineering,Chengdu,610065,China |
Recommended Citation GB/T 7714 | Yang,Ziyuan,Xia,Wenjun,Qiao,Yifan,et al. CO3Net: Coordinate-Aware Contrastive Competitive Neural Network for Palmprint Recognition[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72. |
APA | Yang,Ziyuan., Xia,Wenjun., Qiao,Yifan., Lu,Zexin., Zhang,Bob., Leng,Lu., & Zhang,Yi (2023). CO3Net: Coordinate-Aware Contrastive Competitive Neural Network for Palmprint Recognition. IEEE Transactions on Instrumentation and Measurement, 72. |
MLA | Yang,Ziyuan,et al."CO3Net: Coordinate-Aware Contrastive Competitive Neural Network for Palmprint Recognition".IEEE Transactions on Instrumentation and Measurement 72(2023). |
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