research-article

Explainable AI: A Multispectral Palm-Vein Identification System with New Augmentation Features

Authors:

Chih-Hsien Hsia,

Kai-Lung HuaAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 17, Issue 3s

Article No.: 111, Pages 1 - 21

https://doi.org/10.1145/3468873

Published: 15 November 2021 Publication History

Abstract

Recently, as one of the most promising biometric traits, the vein has attracted the attention of both academia and industry because of its living body identification and the convenience of the acquisition process. State-of-the-art techniques can provide relatively good performance, yet they are limited to specific light sources. Besides, it still has poor adaptability to multispectral images. Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, they often require large training samples and high computation that are infeasible for palm-vein identification. To address this limitation, this work proposes a palm-vein identification system based on lightweight CNN and adaptive multi-spectral method with explainable AI. The principal component analysis on symmetric discrete wavelet transform (SMDWT-PCA) technique for vein images augmentation method is adopted to solve the problem of insufficient data and multispectral adaptability. The depth separable convolution (DSC) has been applied to reduce the number of model parameters in this work. To ensure that the experimental result demonstrates accurately and robustly, a multispectral palm image of the public dataset (CASIA) is also used to assess the performance of the proposed method. As result, the palm-vein identification system can provide superior performance to that of the former related approaches for different spectrums.

References

[1]

Yun-Fu Liu, Jing-Ming Guo, Chih-Hsien Hsia, Sheng-Yao Su, and Hua Lee. 2014. Sample space dimensionality refinement for symmetrical object detection. IEEE Transactions on Information Forensics and Security 9, 11 (2014), 1953–1961.

Digital Library

[2]

Jordi Sanchez-Riera, Kathiravan Srinivasan, Kai-Lung Hua, Wen-Huang Cheng, M. Anwar Hossain, and Mohammed F. Alhamid. 2018. Robust rgb-d hand tracking using deep learning priors. IEEE Transactions on Circuits and Systems for Video Technology 28, 9 (2018), 2289–2301.

[3]

Jing-Ming Guo, Chih-Hsien Hsia, Yun-Fu Liu, Jie-Cyun Yu, Mei-Hui Chu, and Thanh-Nam Le. 2012. Contact-free hand geometry-based identification system. Expert Systems with Applications 39, 14 (2012), 11728–11736.

Digital Library

[4]

Karen P. Hollingsworth, Kevin W. Bowyer, and Patrick J. Flynn. 2011. Improved iris recognition through fusion of hamming distance and fragile bit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 12 (2011), 2465–2476.

Digital Library

[5]

Salil Prabhakar, Anil K. Jain, and Sharath Pankanti. 2003. Learning fingerprint minutiae location and type. Pattern Recognition 36, 8 (2003), 1847–1857.

[6]

Vitomir Štruc and Nikola Pavešić. 2009. Phase congruency features for palm-print verification. IET Signal Processing 3, 4 (2009), 258–268.

[7]

Yan-Ching Lin, Min-Chun Hu, Wen-Huang Cheng, Yung-Huan Hsieh, and Hong-Ming Chen. 2012. Human action recognition and retrieval using sole depth information. In 2012 ACM 20th International Conference on Multimedia. ACM, 1053–1056.

Digital Library

[8]

Hong-Xia Xie, Ling Lo, Hong-Han Shuai, and Wen-Huang Cheng. 2020. AU-assisted graph attention convolutional network for micro-expression recognition. In 2020 ACM 28th International Conference on Multimedia. ACM, 2871–2880.

Digital Library

[9]

Chih-Hsien Hsia. 2017. New verification strategy for finger-vein recognition system. IEEE Sensors Journal 18, 2 (2017), 790–797.

[10]

Parul Arora, Smriti Srivastava, Madasu Hanmandlu, and Sandeep Bhargava. 2018. Robust authentication using dorsal hand vein images. IEEE Intelligent Systems 34, 2 (2018), 25–35.

Digital Library

[11]

Wei Wu, Stephen John Elliott, Sen Lin, and Weiqi Yuan. 2019. Low-cost biometric recognition system based on nir palm vein image. IET Biometrics 8, 3 (2019), 206–214.

[12]

Philip Bontrager, Aditi Roy, Julian Togelius, Nasir Memon, and Arun Ross. 2018. Deepmasterprints: Generating masterprints for dictionary attacks via latent variable evolution. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, 1–9.

[13]

Santosh Tirunagari, Norman Poh, David Windridge, Aamo Iorliam, Nik Suki, and Anthony T. S. Ho. 2015. Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security 10, 4 (2015), 762–777.

Digital Library

[14]

David Menotti, Giovani Chiachia, Allan Pinto, William Robson Schwartz, Helio Pedrini, Alexandre Xavier Falcao, and Anderson Rocha. 2015. Deep representations for iris, face, and fingerprint spoofing detection. IEEE Transactions on Information Forensics and Security 10, 4 (2015), 864–879.

Digital Library

[15]

Mina Farmanbar and Önsen Toygar. 2017. Spoof detection on face and palmprint biometrics. Signal, Image and Video Processing 11, 7 (2017), 1253–1260.

[16]

Jian-Da Wu and Siou-Huan Ye. 2009. Driver identification using finger-vein patterns with radon transform and neural network. Expert Systems with Applications 36, 3 (2009), 5793–5799.

Digital Library

[17]

Wengong Dong, Jianwen Ren, Yahui Su, Chunyi Wang, Xiongwei Sun, Zede Zhu, and Xinhua Zeng. 2017. Research on multi-spectral adaptive method for palm vein capturing based on image quality. In 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 1154–1157.

[18]

Yingbo Zhou and Ajay Kumar. 2011. Human identification using palm-vein images. IEEE Transactions on Information Forensics and Security 6, 4 (2011), 1259–1274.

Digital Library

[19]

Wenxiong Kang and Qiuxia Wu. 2014. Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Transactions on Information Forensics and Security 9, 11 (2014), 1974–1985.

Digital Library

[20]

Emanuela Piciucco, Emanuele Maiorana, and Patrizio Campisi. 2018. Palm vein recognition using a high dynamic range approach. IET Biometrics 7, 5 (2018), 439–446.

[21]

Sin-Ye Jhong, Po-Yen Tseng, Natnuntnita Siriphockpirom, Chih-Hsien Hsia, Ming-Shih Huang, Kai-Lung Hua, and Yung-Yao Chen. 2020. An Automated biometric identification system using cnn-based palm vein recognition. In 2020 IEEE 8th International Conference on Advanced Robotics and Intelligent Systems (ARIS). IEEE, 1–6.

[22]

Ajay Kumar and Yingbo Zhou. 2011. Human identification using finger images. IEEE Transactions on Image Processing 21, 4 (2011), 2228–2244.

Digital Library

[23]

Jong Min Song, Wan Kim, and Kang Ryoung Park. 2019. Finger-vein recognition based on deep densenet using composite image. IEEE Access 7 (2019), 66845–66863.

[24]

Cihui Xie and Ajay Kumar. 2017. Finger vein identification using convolutional neural network and supervised discrete hashing. In Deep Learning for Biometrics. Springer, 109–132.

[25]

Jun Wang, Zaiyu Pan, Guoqing Wang, Ming Li, and Yulian Li. 2018. Spatial pyramid pooling of selective convolutional features for vein recognition. IEEE Access 6 (2018), 28563–28572.

[26]

J. Enrique Suarez Pascual, Jaime Uriarte-Antonio, Raul Sanchez-Reillo, and Michael G. Lorenz. 2010. Capturing hand or wrist vein images for biometric authentication using low-cost devices. In 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE, 318–322.

Digital Library

[27]

Ö. Toygar, F. O. Babalola, and Y. Bi̇ti̇ri̇m. 2020. FYO: A novel multimodal vein database with palmar, dorsal and wrist biometrics. IEEE Access 8 (2020), 82461–82470.

[28]

Hengyi Zhang, Chaoying Tang, Adams Wai-Kin Kong, and Noah Craft. 2012. Matching vein patterns from color images for forensic investigation. In 2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE, 77–84.

[29]

Naoto Miura, Akio Nagasaka, and Takafumi Miyatake. 2004. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications 15, 4 (2004), 194–203.

Digital Library

[30]

Leila Mirmohamadsadeghi and Andrzej Drygajlo. 2014. Palm vein recognition with local texture patterns. IET Biometrics 3, 4 (2014), 198–206.

[31]

Xueyan Li, Shuxu Guo, Fengli Gao, and Ye Li. 2007. Vein pattern recognitions by moment invariants. In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 612–615.

[32]

Wenxiong Kang, Yang Liu, Qiuxia Wu, and Xishun Yue. 2014. Contact-free palm-vein recognition based on local invariant features. PloS One 9, 5 (2014), e97548.

[33]

Guoqing Wang and Jun Wang. 2017. Sift based vein recognition models: Analysis and improvement. Computational and Mathematical Methods in Medicine 17 (2017), 1–14.

[34]

Syafeeza Ahmad Radzi, Mohamed Khalil Mani, and Rabia Bakhteri. 2016. Finger-vein biometric identification using convolutional neural network. Turkish Journal of Electrical Engineering & Computer Sciences 24, 3 (2016), 1863–1878.

[35]

Patrice Y. Simard, David Steinkraus, and John C. Platt. 2003. Best practices for convolutional neural networks applied to visual document analysis. In Icdar 3, 2003.

Digital Library

[36]

Hyung Gil Hong, Min Beom Lee, and Kang Ryoung Park. 2017. Convolutional neural network-based finger-vein recognition using nir image sensors. Sensors 17, 6 (2017), 1297.

[37]

Karen Simonyan, and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR). 77–84.

[38]

Das Emanuela Piciucco, Emanuele Maiorana, and Patrizio Campisi. 2018. Convolutional neural network for finger-vein- vein-based biometric identification. IEEE Transactions on Information Forensics and Security 14, 2 (2018), 360–373.

[39]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM 60, 6 (2017), 84–90.

Digital Library

[40]

Fang Yuxun, Wu Qiuxia, and Kang Wenxiong. 2018. A novel finger vein verification system based on two-stream convolutional network learning. Neurocomputing 290, 17 (2018), 100–107.

[41]

Xiang Li, Shuo Chen, Xiaolin Hu, and Jian Yang. 2019. Understanding the disharmony between dropout and batch normalization by variance shift. In 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2677–2685.

[42]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. Why should I trust you?" explaining the predictions of any classifier. In 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135–1144.

Digital Library

[43]

CASIA public database. Retrieved from http://biometrics.idealtest.org/.

[44]

E. D. Cubuk, B. Zoph, D. Mané, V. Vasudevan, and Q. V. Le. 2019. AutoAugment: learning augmentation strategies from data. 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 113–123.

[45]

E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le. 2020. Randaugment: Practical automated data augmentation with a reduced search space. 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 3008–3017.

[46]

Shruti Bhilare, Gaurav Jaswal, Vivek Kanhangad, and Aditya Nigam. 2018. Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach. Machine Vision and Applications 18, 7 (2018), 1269–1286.

Digital Library

[47]

Daksh Thapar, Gaurav Jaswal, Aditya Nigam, and Vivek Kanhangad. 2019. Pvsnet: Palm-vein authentication Siamese network trained using triplet loss and adaptive hard mining by learning enforced domain specific features. In 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA). IEEE, 1–8.

Cited By

Liu YGe PWang GLiu QHuang D(2025)Multi-Grained Contrastive Learning for Text-supervised Open-vocabulary Semantic SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3711868Online publication date: 10-Jan-2025
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Yang YJoukovsky BOramas Mogrovejo JTuytelaars TDeligiannis N(2024)SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366524820:8(1-29)Online publication date: 13-Jun-2024
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Yu XWu KYang YLiu Q(2024)WaRENet: A Novel Urban Waterlogging Risk Evaluation NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365116320:7(1-28)Online publication date: 16-May-2024
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Index Terms

Explainable AI: A Multispectral Palm-Vein Identification System with New Augmentation Features
1. Security and privacy
  1. Security services
    1. Authentication
      1. Biometrics

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 3s

October 2021

324 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3492435

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Association for Computing Machinery

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Publication History

Published: 15 November 2021

Accepted: 01 May 2021

Revised: 01 May 2021

Received: 01 November 2020

Published in TOMM Volume 17, Issue 3s

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Cited By

Liu YGe PWang GLiu QHuang D(2025)Multi-Grained Contrastive Learning for Text-supervised Open-vocabulary Semantic SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3711868Online publication date: 10-Jan-2025
https://doi.org/10.1145/3711868
Yang YJoukovsky BOramas Mogrovejo JTuytelaars TDeligiannis N(2024)SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366524820:8(1-29)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3665248
Yu XWu KYang YLiu Q(2024)WaRENet: A Novel Urban Waterlogging Risk Evaluation NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365116320:7(1-28)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3651163
Liu WCai JLi QLiao CCao JHe SYu Y(2024)Learning Nighttime Semantic Segmentation the Hard WayACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365003220:7(1-23)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3650032
Shi TCai ZLi JGao H(2024)Optimize the Age of Useful Information in Edge-assisted Energy-harvesting Sensor NetworksACM Transactions on Sensor Networks10.1145/364034220:2(1-26)Online publication date: 16-Feb-2024
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Yang JWang ZWang GHuang BYang YTu W(2024)Auxiliary Information Guided Self-attention for Image Quality AssessmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363571620:4(1-23)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3635716
Albano RGiusti LMaiorana ECampisi P(2024)Explainable Vision Transformers for Vein Biometric RecognitionIEEE Access10.1109/ACCESS.2024.339355812(60436-60446)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3393558
Tucci CDella Greca ATortora GFrancese R(2024)Explainable biometrics: a systematic literature reviewJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-024-04856-1Online publication date: 19-Sep-2024
https://doi.org/10.1007/s12652-024-04856-1
Lu SFung SPan WWickramasinghe NLu X(2024)Veintr: robust end-to-end full-hand vein identification with transformerThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-024-03286-640:10(7015-7023)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s00371-024-03286-6
Hsia CKe LChen S(2023)Improved Lightweight Convolutional Neural Network for Finger Vein Recognition SystemBioengineering10.3390/bioengineering1008091910:8(919)Online publication date: 3-Aug-2023
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