Abstract
In the current trend towards contactless recognition, palmprint biometrics has proven to be very effective due to its uniqueness, reliability, acceptability, non-intrusiveness and low cost of acquisition devices. Palmprint biometrics can be used in many situations by simply capturing the hand with the camera of a mobile device. However, its use in real-life situations adds a high variability to the capturing conditions, increasing the complexity of the recognition process and causing failure of many processing methods up to date. In this study, a deep-learning algorithm is proposed to detect the palmprint region of interest and rotating the image as needed for the subsequent feature extraction on it. For this purpose, a convolutional neural network (CNN) has been trained and evaluated with 2445 hand images from 6 different databases that cover diverse environmental conditions. Results show that this algorithm provides an averaged F 1-score of 89% in images with complex backgrounds, dim light or varied hand arrangements; and it correctly processes images in which users are wearing rings, something that traditional segmentation cannot handle. Some conditions such as hard shadowing remain very complex for this algorithm, but it could be highly improved by increasing the volume of training datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
B. Ríos-Sánchez, M. Viana-Matesanz, C. Sánchez-Ávila, Multibiometría de huella palmar y geometría de mano para un sistema sin contacto orientado a dispositivos móviles, in XXXI Simposium Nacional de la Union Científica Internacional de Radio (URSI2016), 2016.
W. Chen, Y. Chiang, Y. Chiu, Biometric verification by fusing hand geometry and palmprint, in Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), vol. 2, (2007), pp. 403–406. https://doi.org/10.1109/IIHMSP.2007.4457734
J. Guerra-Casanova, B. Ríos-Sánchez, M. Viana-Matesanz, G. Bailador, C. Sánchez-Ávila, M.J. Melcón De Giles, Comfort and security perception of biometrics in mobile phones with widespread sensors, in 2016 IEEE 35th Symposium on Reliable Distributed Systems Workshops (SRDSW), (2016), pp. 13–18. https://doi.org/10.1109/SRDSW.2016.13
G. Bailador, B. Ríos-Sánchez, R. Sánchez-Reillo, H. Ishikawa, C. Sánchez-Ávila, Flooding-based segmentation for contact-less hand biometrics oriented to mobile devices. IET Biom. 7, 431–438 (2017). https://doi.org/10.1049/iet-bmt.2017.0166
B. Ríos-Sanchez, M. Viana-Matesanz, C. Sánchez-Ávila, A comparative study of palmprint feature extraction methods for contact-less biometrics under different environmental conditions, in 2017 International Carnahan Conference on Security Technology (ICCST), (2017), pp. 1–6
C. Poon, D.C. Wong, H.C. Shen, A new method in locating and segmenting palmprint into region-of-interest, in Proceedings of the 17th International Conference on Pattern Recognition (ICPR IEEE), vol. 4, (2004), pp. 533–536
A. Kong, D. Zhang, M. Kamel, A survey of palmprint recognition. Pattern Recogn. 42(7), 1408–1418 (2009)
B. Ríos-Sánchez, M. Viana-Matesanz, C. Sánchez-Ávila, Curvelets for contact-less hand biometrics under varied environmental conditions, in 2017 International Carnahan Conference on Security Technology (ICCST), (2017), pp. 1–6. https://doi.org/10.1109/CCST.2017.8167805
K. Luo, D. Zhong, Robust and adaptive region of interest extraction for unconstrained palmprint recognition. J. Electron. Imaging 30(3), 033005 (2021). https://doi.org/10.1117/1.JEI.30.3.033005
Z.Q. Zhao, P. Zheng, S.T. Xu, X. Wu, Object detection with deep learning: A review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2009)
M. Izadpanahkakhk, S. Razavi, M. Taghipour-Gorjikolaie, S. Zahiri, A. Uncini, Deep region of interest and feature extraction models for palmprint verification using convolutional neural networks transfer learning. Appl. Sci. 8(7), 1210 (2009)
V. Kanhangad, A. Kumar, D. Zhang, A unified framework for contactless hand verification. IEEE Trans. Inf. Forensics Secur. 6(3), 1014–1027 (2011)
W. Gong, X. Zhang, B. Deng, X. Xu, Palmprint recognition based on convolutional neural network-Alexnet, in 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), (2019), pp. 313–316
K. Prihodova, M. Hub, Hand-based biometric system using convolutional neural networks. Acta Inform. Pragensia 9(1), 48–57 (2020)
Y. Liu, A. Kumar, A deep learning based framework to detect and recognize humans using contactless palmprints in the wild. arXiv preprint arXiv:1812.11319, 2018
K. He, G. Gkioxari, P. Dollar, R. Girshick, Mask R-CNN, in 2017 IEEE International Conference on Computer Vision (ICCV), (2017), pp. 2980–2988. https://doi.org/10.1109/ICCV.2017.322
S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Proces. Syst. 28(2015), 91–99 (2015)
S. Looi, Rotated mask R-CNN: From bounding boxes to rotated bounding boxes, 2019. Available online: https://github.com/mrlooi/rotated maskrcnn (Accessed on 24 Oct 2020)
R. Khandelwal, COCO and Pascal VOC data format for object detection, 2019. Available online: https://towardsdatascience.com/coco-data-format-for-object-detection-a4c5eaf518c5 (Accessed on 18 Jan 2021)
D.T. Larose, C.D. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd edn. (John Wiley & Sons, New Jersey, 2014)
J. Brownlee, How to configure the learning rate when training deep learning neural networks, 2019. Available online: https://machinelearningmastery.com/learning-rate-for-deep-learning-neural-networks (Accessed on 17 May 2021)
A. de Santos-Sierra, J. Guerra-Casanova, C. Sanchez-Avila, V. Jara-Vera, Silhouette-based hand recognition on mobile devices, in 43rd Annual 2009 International Carnahan Conference on Security Technology, (2009), pp. 160–166
B. Ríos-Sánchez, D. Costa-da-Silva, N. Martín-Yuste, C. Sanchez-Ávila, Deep learning for facial recognition on single sample per person scenarios with varied capturing conditions. Appl. Sci. 9(24), 5474 (2019). https://doi.org/10.3390/app9245474
B. Ríos-Sánchez, M.F. Arriaga-Gomez, J. Guerra-Casanova, D. de Santos-Sierra, I. de Mendizábal-Vázquez, G. Bailador, C. Sánchez-Ávila, gb2sμMOD: A MUltiMODal biometric video database using visible and IR light. Inf. Fusion 32, 64–79 (2016)
Acknowledgement
The authors would like to thank Hong Kong Polytechnic University for the use of PolyU 2D Palmprint Database as well as all the contributors who have participated in the creation of our proprietary databases for their patience and cooperation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sánchez, B.M., Sánchez, B.R., Ávila, C.S. (2022). Deep Learning for Palmprint Detection and Alignment on Biometric Systems. In: Daimi, K., Francia III, G., Encinas, L.H. (eds) Breakthroughs in Digital Biometrics and Forensics. Springer, Cham. https://doi.org/10.1007/978-3-031-10706-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-10706-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10705-4
Online ISBN: 978-3-031-10706-1
eBook Packages: Computer ScienceComputer Science (R0)