A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism
Article No.: 20, Pages 1 - 9
Abstract
Fingerprint recognition systems are highly dependent on the quality and accuracy of the fingerprint representation. Traditional fingerprint recognition algorithms often employ handcrafted features designed manually based on domain knowledge and heuristics. However, these methods may struggle to capture the intricate patterns and minute details present in fingerprints, leading to limited discriminatory power and vulnerability to noise and variations in imaging conditions. This paper presents a novel approach for learning robust fingerprint representation by leveraging the power of deep convolution networks (DCN) along with attention mechanisms. The proposed method consists of a dual-module framework. The first module extracts local features, i.e., minutiae-based features, and the second module focuses on extracting global features, i.e., texture-based features. The extracted local and global features are concatenated into a single fixed-length feature vector. It aims to capture fine-grained details and complex patterns present in fingerprint images, thereby enhancing the accuracy and robustness of fingerprint recognition systems. Experimental results show that the proposed method outperforms state-of-the-art (SOTA) fingerprint recognition performances across a variety of benchmark datasets, containing fingerprints from various sensors and having different resolutions and sizes. It harnesses the finer features of fingerprints, as well as efficiently boosts the performance of the framework to progress SOTA.
References
[1]
Marwin B Alejo. 2021. Unconstrained ear recognition using transformers. Jordanian Journal of Computers and Information Technology 7, 4 (2021).
[2]
Laith Alzubaidi, Jinglan Zhang, Amjad J Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, José Santamaría, Mohammed A Fadhel, Muthana Al-Amidie, and Laith Farhan. 2021. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data 8 (2021), 1–74.
[3]
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).
[4]
Sylvain Bernard, Nozha Boujemaa, David Vitale, and Claude Bricot. 2002. Fingerprint segmentation using the phase of multiscale gabor wavelets. In The 5th Asian Conference on Computer Vision, Melbourne, Australia. Citeseer.
[5]
Raffaele Cappelli, Matteo Ferrara, Annalisa Franco, and Davide Maltoni. 2007. Fingerprint verification competition 2006. Biometric Technology Today 15, 7-8 (2007), 7–9.
[6]
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40, 4 (2017), 834–848.
[7]
Paula Delgado-Santos, Ruben Tolosana, Richard Guest, Farzin Deravi, and Ruben Vera-Rodriguez. 2023. Exploring transformers for behavioural biometrics: A case study in gait recognition. Pattern Recognition (2023), 109798.
[8]
Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding. 2022. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 11963–11975.
[9]
Joshua J Engelsma, Kai Cao, and Anil K Jain. 2019. Learning a fixed-length fingerprint representation. IEEE transactions on pattern analysis and machine intelligence 43, 6 (2019), 1981–1997.
[10]
Joshua James Engelsma, Steven Grosz, and Anil K Jain. 2022. PrintsGAN: Synthetic fingerprint generator. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 5 (2022), 6111–6124.
[11]
Yulin Feng and Ajay Kumar. 2023. Detecting locally, patching globally: An end-to-end framework for high speed and accurate detection of fingerprint minutiae. IEEE Transactions on Information Forensics and Security 18 (2023), 1720–1733.
[12]
Steven A Grosz, Joshua J Engelsma, Rajeev Ranjan, Naveen Ramakrishnan, Manoj Aggarwal, Gerard G Medioni, and Anil K Jain. 2022. Minutiae-guided fingerprint embeddings via vision transformers. arXiv preprint arXiv:2210.13994 (2022).
[13]
Shan Gu, Jianjiang Feng, Jiwen Lu, and Jie Zhou. 2022. Latent fingerprint indexing: Robust representation and adaptive candidate list. IEEE Transactions on Information Forensics and Security 17 (2022), 908–923.
[14]
Meng-Hao Guo, Tian-Xing Xu, Jiang-Jiang Liu, Zheng-Ning Liu, Peng-Tao Jiang, Tai-Jiang Mu, Song-Hai Zhang, Ralph R Martin, Ming-Ming Cheng, and Shi-Min Hu. 2022. Attention mechanisms in computer vision: A survey. Computational visual media 8, 3 (2022), 331–368.
[15]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision. 1026–1034.
[16]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
[17]
Dan Hendrycks and Kevin Gimpel. 2016. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016).
[18]
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012).
[19]
Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7132–7141.
[20]
Bhavin Jawade, Akshay Agarwal, Srirangaraj Setlur, and Nalini Ratha. 2021. Multi loss fusion for matching smartphone captured contactless finger images. In 2021 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 1–6.
[21]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).
[22]
Harold W Kuhn. 1955. The Hungarian method for the assignment problem. Naval research logistics quarterly 2, 1-2 (1955), 83–97.
[23]
Ruilin Li, Dehua Song, Yuhang Liu, and Jufu Feng. 2019. Learning global fingerprint features by training a fully convolutional network with local patches. In 2019 International Conference on Biometrics (ICB). IEEE, 1–8.
[24]
Wei Li, Kai Liu, Lizhe Zhang, and Fei Cheng. 2020. Object detection based on an adaptive attention mechanism. Scientific Reports 10, 1 (2020), 11307.
[25]
Chenhao Lin and Ajay Kumar. 2018. A CNN-based framework for comparison of contactless to contact-based fingerprints. IEEE Transactions on Information Forensics and Security 14, 3 (2018), 662–676.
[26]
Feng Liu, Zhe Kong, Haozhe Liu, Wentian Zhang, and Linlin Shen. 2022. Fingerprint presentation attack detection by channel-wise feature denoising. IEEE Transactions on Information Forensics and Security 17 (2022), 2963–2976.
[27]
Haojie Ma, Wenzhong Li, Xiao Zhang, Songcheng Gao, and Sanglu Lu. 2019. AttnSense: Multi-level attention mechanism for multimodal human activity recognition. In IJCAI. 3109–3115.
[28]
Shervin Minaee, Elham Azimi, and Amirali Abdolrashidi. 2019. Fingernet: Pushing the limits of fingerprint recognition using convolutional neural network. arXiv preprint arXiv:1907.12956 (2019).
[29]
Neurotechnology. 2023. VeriFinger SDK. Retrieved August 07, 2023 from https://www.neurotechnology.com/verifinger.html
[30]
Dinh-Luan Nguyen, Kai Cao, and Anil K Jain. 2018. Robust minutiae extractor: Integrating deep networks and fingerprint domain knowledge. In 2018 International Conference on Biometrics (ICB). IEEE, 9–16.
[31]
Halil İbrahim Öztürk, Berkay Selbes, and Yusuf Artan. 2022. Minnet: Minutia patch embedding network for automated latent fingerprint recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1627–1635.
[32]
Fagul Pandey, Priyabrata Dash, Debasis Samanta, and Monalisa Sarma. 2021. ASRA: Automatic singular value decomposition-based robust fingerprint image alignment. Multimedia Tools and Applications 80 (2021), 15647–15675.
[33]
Liaojun Pang, Jiong Chen, Fei Guo, Zhicheng Cao, Eryun Liu, and Heng Zhao. 2022. ROSE: real one-stage effort to detect the fingerprint singular point based on multi-scale spatial attention. Signal, Image and Video Processing 16, 3 (2022), 669–676.
[34]
Additya Popli, Saraansh Tandon, Joshua J Engelsma, and Anoop Namboodiri. 2023. A unified model for fingerprint authentication and presentation attack detection. In Handbook of Biometric Anti-Spoofing: Presentation Attack Detection and Vulnerability Assessment. Springer, 77–99.
[35]
Nagisa Sasuga, Koichi Ito, and Takafumi Aoki. 2022. Fingerprint Feature Extraction Using CNN with Multiple Attention Mechanisms. In 2022 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 1–8.
[36]
Dehua Song, Yao Tang, and Jufu Feng. 2019. Aggregating minutia-centred deep convolutional features for fingerprint indexing. Pattern Recognition 88 (2019), 397–408.
[37]
Giuseppe Stragapede, Paula Delgado-Santos, Ruben Tolosana, Ruben Vera-Rodriguez, Richard Guest, and Aythami Morales. 2023. Mobile keystroke biometrics using transformers. In 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG). IEEE, 1–6.
[38]
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818–2826.
[39]
Ai Takahashi, Yoshinori Koda, Koichi Ito, and Takafumi Aoki. 2020. Fingerprint feature extraction by combining texture, minutiae, and frequency spectrum using multi-task CNN. In 2020 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 1–8.
[40]
Hanzhuo Tan and Ajay Kumar. 2021. Minutiae attention network with reciprocal distance loss for contactless to contact-based fingerprint identification. IEEE Transactions on Information Forensics and Security 16 (2021), 3299–3311.
[41]
Saraansh Tandon and Anoop Namboodiri. 2022. Transformer based fingerprint feature extraction. In 2022 26th International Conference on Pattern Recognition (ICPR). IEEE, 870–876.
[42]
Yao Tang, Fei Gao, Jufu Feng, and Yuhang Liu. 2017. FingerNet: An unified deep network for fingerprint minutiae extraction. In 2017 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 108–116.
[43]
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, 2018. Deep learning for computer vision: A brief review. Computational intelligence and neuroscience 2018 (2018).
[44]
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV). 3–19.
[45]
Song Wu, Bing Liu, Zheng Wang, Zexi Jia, and Jufu Feng. 2022. Minutiae-awarely Learning Fingerprint Representation for Fingerprint Indexing. In 2022 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 1–8.
[46]
Tong Wu, Junshi Huang, Guangyu Gao, Xiaoming Wei, Xiaolin Wei, Xuan Luo, and Chi Harold Liu. 2021. Embedded discriminative attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 16765–16774.
[47]
André Brasil Vieira Wyzykowski and Anil K Jain. 2023. Synthetic latent fingerprint generator. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 971–980.
[48]
Yaoyao Zhong and Weihong Deng. 2021. Face transformer for recognition. arXiv preprint arXiv:2103.14803 (2021).
Index Terms
- A Novel Framework for Robust Fingerprint Representations using Deep Convolution Network with Attention Mechanism
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Published: 31 January 2024
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ICVGIP '23: Indian Conference on Computer Vision, Graphics and Image Processing
December 15 - 17, 2023
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