A Mobile Palmprint Authentication System Using a Modified MNT Algorithm, Circular Local Binary Pattern, and CNN (mobileNet)

Jide Kehinde Adeniyi, Tinuke Omolewa Oladele, Ayodele Adebiyi, Marion Adebiyi, Tunde Taiwo Adeniyi

Abstract


A few approaches have been proposed for hand segmentation in palmprint recognition. Skin-color information does not process sufficient information for discrimination in complex backgrounds and variable illumination. The use of guides has also been proposed, which restricts hand placement during capturing. Contour tracing algorithms have also been proposed in the literature. This worked in an even background scenario with no objects or patterns around the hand. In the case of uneven background with objects present, the traditional contour tracing algorithm cannot accurately segment the hand from the background. Hence, this paper proposes a modified Moore Neighbor Tracing (MNT) algorithm for hand detection and key-point extraction in complex backgrounds. The hand image is converted to grey, and the edges in the hand image are detected. The modified algorithm then transverses selected edges and returns the peak and valleys of each finger. This is then used to crop the palm. The modified algorithm improves the accuracy of hand detection in complex backgrounds with an F-Score of 0.8657. A mobile palmprint biometric system was also presented using Circular Local Binary Pattern (CLBP) and Convolutional Neural Network (CNN). The system showed an accuracy of 98.3% for hands captured with the mobile device and the CASIA online database. An accuracy of 99.0% was also recorded for GPDS and PolyU online databases.

Keywords


Hand segmentation; improved contour tracing algorithm; feature extraction; complex background, convolutional neural network

Full Text:

PDF

References


D. Izergin and M. Eremeev, “Risk assessment model of compromising personal data on mobile devices,†E3S Web Conf., vol. 270, 2021, doi: 10.1051/e3sconf/202127001013.

S. Sharma, R. Kumar, and C. R. Krishna, “A survey on analysis and detection of Android ransomware,†Concurr. Comput. Pract. Exp., doi: 10.1002/cpe.6272.

J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Cell phone-based biometric identification,†in IEEE 4th Int. Conf. Biometrics Theory, Appl. Syst. BTAS 2010, 2010, pp. 1–7.

L. Long, “Biometrics : The Future of Mobile Phones,†End User Comput. Serv.,†End User Comput. Serv., pp. 1–5, 2013.

R. Ryu, S. Yeom, S.-H. Kim, and D. Herbert, “Continuous Multimodal Biometric Authentication Schemes: A Systematic Review,†IEEE Access, vol. 9, pp. 34541–34557, 2021, doi: 10.1109/ACCESS.2021.3061589.

A. Giełczyk, M. Choras, and R. Kozik, “Lightweight verification schema for image-based palmprint biometric systems,†Mob. Inf. Syst., vol. 2019, 2019, doi: 10.1155/2019/2325891.

U. Saeed, K. Masood, and H. Dawood, “Illumination normalization techniques for makeup-invariant face recognition,†Comput. Electr. Eng., vol. 89, 2021, doi: 10.1016/j.compeleceng.2020.106921.

G. Bailadoar, B. Ríos-Sánchez, R. Sánchez-Reillo, H. Ishikawa, and C. Sánchez-Ãvila, “Flooding-based segmentation for contactless hand biometrics oriented to mobile devices,†IET Biometrics, vol. 7, no. 5, pp. 431–438, 2018, doi: 10.1049/iet-bmt.2017.0166.

F. Gao, K. Cao, L. Leng, and Y. Yuan, “Mobile Palmprint Segmentation Based on Improved Active Shape Model,†J. Multimed. Inf. Syst., vol. 5, no. 4, pp. 221–228, 2018.

L. Leng, F. Gao, Q. Chen, and C. Kim, “Palmprint recognition system on mobile devices with double-line-single-point assistance,†Pers. Ubiquitous Comput., vol. 22, no. 1, pp. 93–104, 2018, doi: 10.1007/s00779-017-1105-2.

N. M. S. Hussein, S. M. Hammed, B. Ergen, and P. G. Student, “Biometric Identification System based on Hand Geometry,†Int. J. Innov. Res. Sci., vol. 6, no. 3, pp. 3159–3166, 2017, doi: 10.15680/IJIRSET.2017.0603005.

M. Akmal-Jahan, B. Jasmine, and V. Chandran, “Minutiae-Triangle-Graph : An invariant feature representation for fingerprints and palmprints in hand biometrics,†Springer, 2020.

G. Jaswal, A. Kaul, and R. Nath, Multimodal Biometric Recognition System Using Hand Shape , Palm Print , and Hand Geometry, vol. II. Springer Singapore, 2019.

W. M. Matkowski, T. Chai, and A. W. K. Kong, “Palmprint Recognition in Uncontrolled and Uncooperative Environment,†IEEE Trans. Inf. Forensics Secur., vol. 15, pp. 1601–1615, 2020, doi: 10.1109/TIFS.2019.2945183.

A. Ungureanu, S. Salahuddin, and A. Member, “Toward Unconstrained Palmprint Recognition on Consumer Devices : A Literature Review,†IEEE Access, vol. 8, pp. 86130–86148, 2020, doi: 10.1109/ACCESS.2020.2992219.

M. M. H. Ali, V. H. Mahale, P. L. Yannawar, and A. T. Gaikwad, “A Review: Palmprint Recognition Process and Techniques,†Int. J. Appl. Eng. Res., vol. 13, no. 10, pp. 7499–7507, 2018.

C. Naveena, R. Shreyas, and K. Chethan, “Texture Features in Palmprint Recognition System,†Int. J. Nat. Comput. Res., vol. 10, pp. 41–57, doi: 10.4018/IJNCR.2021010104.

M. Aguado-Martínez, J. Hernández-Palancar, K. Castillo-Rosado, and E. Al., “Document scanners for minutiae-based palmprint recognition: a feasibility study,†Pattern Anal. Appl., vol. 24, pp. 459–472, 2021, doi: 10.1007/s10044-020-00923-3.

M. P.Dale, M. A. Joshi, and H. J. Galiyawala, “A Single Sensor Hand Geometry and Palm Texture Fusion for Person Identification,†Int. J. Comput. Appl., vol. 42, no. 7, pp. 11–16, 2012, doi: 10.5120/5703-7726.

S. Aoyama, K. Ito, and T. Aoki, “A Contactless Palmprint Recognition Algorithm for Mobile Phones,†Int. Work. Adv. Image Technol., pp. 409–413, 2013.

T. Wu and L. Leng, “Video Palmprint Recognition System Based on Modified Double-line-single-point Assisted Placement,†J. Multimed. Inf. Syst., vol. 8, no. 1, pp. 20–30, 2021.

T. O. Oladele, K. Adeniyi, and T. O. Aro, “Framework for User Authentication at a Distance for Mobile Phones Using Contactless Hand-based Multimodal Biometric System,†J. Comput. Sci. Control Syst., vol. 12, no. 1, pp. 24–27, 2019.

L. Fei, G. Lu, W. Jia, S. Teng, and D. Zhang, “Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation,†IEEE Trans. Syst. Man, Cybern. Syst., vol. 49, no. 2, pp. 346–363, 2019, doi: 10.1109/TSMC.2018.2795609.

S. Verma and S. Chandran, “Contactless Palmprint Verification System using 2-D Gabor Filter and Principal Component Analysis,†Int. Arab J. Inf. Technol., vol. 16, no. 1, 2019.

S. Barra, M. De Marsico, M. Nappi, F. Narducci, and D. Riccio, “A hand-based biometric system in visible light for mobile environments,†Inf. Sci. (Ny)., vol. 479, pp. 472–485, 2019, doi: 10.1016/j.ins.2018.01.010.

P. Poonia, P. K. Ajmera, and V. Shende, “ScienceDirect ScienceDirect Palmprint Recognition using Robust Template Matching Palmprint Recognition using Robust Template Matching,†Procedia Comput. Sci., vol. 167, no. 2019, pp. 727–736, 2020, doi: 10.1016/j.procs.2020.03.338.

N. Xu, Q. Zhu, X. Xu, and D. Zhang, “An effective recognition approach for contactless palmprint,†Vis. Comput., 2020, doi: 10.1007/s00371-020-01962-x.

J. P. Patil and C. S. Pawar, “Palmprint based Pattern Recognition Using Fast ICA,†IEE Xplore, no. ICICCS, pp. 566–569, 2020.

X. Zhou, K. Zhou, and L. Shen, “Rotation and Translation Invariant Palmprint Recognition With Biologically Inspired Transform,†IEEE Access, vol. 8, pp. 80097–80119, 2020, doi: 10.1109/ACCESS.2020.2990736.

P. Kavipriya, M. R. Ebenezar-Jebarani, T. Vino, and G. Jegan, “Ear biometric for personal identification using canny edge detection algorithm and contour tracking method,†2021, doi: 10.1016/j.matpr.2021.03.351.

S. Kumar, A. K. Upadhyay, P. Dubey, and S. Varshney, “Comparative analysis for Edge Detection Techniques,†in 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2021, pp. 675–681, doi: 10.1109/ICCCIS51004.2021.9397225.

S. D. Lokmanwar and A. S. Bhalchandra, “Contour detection based on gaussian filter,†doi: 10.1109/iceca.2019.8822189.

D. N. Lohare, R. R. Manza, and N. Tiwari, “Comparative Study of Prewitt and Canny Edge Detector Using Image Processing Techniques,†2021, doi: 10.1007/978-981-15-6014-9_86.

A. Kumar and S. S. Sodhi, “Comparative Analysis of Gaussian Filter, Median Filter and Denoise Autoenocoder,†in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), 2020, pp. 45–51, doi: 10.23919/INDIACom49435.2020.9083712.

S. Vijayarani and A. Sakila, “Face Recognition based Student Attendance System,†Int. J. Res. Publ. Rev., vol. 2, no. 4, pp. 289–299, 2020.

A. S. Ahmed, “Comparative Study Among Sobel, Prewitt and Canny Edge Detection Operators used in Image Processing,†J. Theor. Appl. Inf. Technol., vol. 96, no. 19, 2018.

T. H. Mandee, M. I. Ahmad, and M. N. M. Isa, “Palmprint Region of Interest Cropping Based on Moore-Neighbor Tracing Algorithm,†Sens. Imaging, vol. 19, p. 15, 2018, doi: 10.1007/s11220-018-0199-6.

R. Priyadharsini and T. S. Sharmila, “Object Detection In Underwater Acoustic Images Using Edge Based Segmentation Method,†Procedia Comput. Sci., vol. 165, pp. 759–765, 2019, doi: 10.1016/j.procs.2020.01.015.

I. Ullah, M. S. Azmi, M. I. Desa, and Y. M. Alomari, “Segmentation of Touching Arabic Characters in Handwritten Documents by Overlapping Set Theory and Contour Tracing,†Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 5, 2019.

S. S. Mansouri, M. Castaño, C. Kanellakis, and G. Nikolakopoulos, “Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection,†in In: Tzovaras D., Giakoumis D., Vincze M., Argyros A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science, 2019, vol. 11754, doi: 10.1007/978-3-030-34995-0_16.

S. Sadhukhan, N. Upadhyay, and P. Chakraborty, “Breast Cancer Diagnosis Using Image Processing and Machine Learning,†2020, doi: 10.1007/978-981-13-7403-6_12.

T. Matić, I. Aleksi, Ž. Hocenski, and D. Kraus, “Real-time biscuit tile image segmentation method based on edge detection,†ISA Trans., vol. 76, pp. 246–254, 2018.

W. Wang, Y. Li, T. Zou, X. Wang, J. You, and Y. Luo, “A Novel Image Classification Approach via Dense-MobileNet Models,†Mob. Inf. Syst., 2020, doi: https://doi.org/10.1155/2020/7602384.

P. Liu, X. Li, H. Cui, S. Li, and Y. Yuan, “Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning,†vol. 2019, pp. 25–28, 2019.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew,†arXiv, 2017.

“CASIA Palmprint Database.†[Online]. Available: http://www.cbsr.ia.ac.cn/english/Palmprint%20Databases.asp. [Accessed: 22-Feb-2023].

“GPDS Hand Database.†[Online]. Available: . https://gpds.ulpgc.es/downloadnew/download.htm. [Accessed: 22-Feb-2023].

“PolyU Multispectral Palmprint Database.†[Online]. Available: . http://www4.comp.polyu.edu.hk/~csajaykr/database.php. [Accessed: 22-Feb-2023].

P. Poonia, P. K. Ajmera, and V. Shende, “Palmprint Recognition using Robust Template Matching Palmprint Recognition using Robust Template Matching,†Procedia Comput. Sci., vol. 167, no. 2019, pp. 727–736, 2020, doi: 10.1016/j.procs.2020.03.338.




DOI: http://dx.doi.org/10.18517/ijaseit.13.2.16136

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development