A GUI-Based Peg-Free Hand Geometry Recognition for Biometric Access Control using Artificial Neural Network

Kazeem B. Adedeji; Oluwatimilehi A. Esan

doi:10.38032/jea.2022.04.001

Authors

Kazeem B. Adedeji Department of Electrical & Electronics Engineering, The Federal University of Technology, Akure, Nigeria
Oluwatimilehi A. Esan Department of Computer Engineering, The Federal University of Technology, Akure, Nigeria

DOI:

https://doi.org/10.38032/jea.2022.04.001

Keywords:

Access Control, Artificial Neural Network, Biometric, Hand Geometry, Security

Abstract

Hand geometry has been a widely used biometric authentication because it is generally believed that the human hand has sufficient anatomical features which could be used for personal identification. Many hand geometry systems use pegs, which guide hand placement on the scanner. The system prompts the user to position the hand on the scanner several times and only captures when the current position is satisfied. In such a system, measurements are not very precise and this reduces accuracy during feature extraction. The system also has a higher false acceptance rate. This paper presents a peg-free hand geometry recognition system that does not depend on the orientation of the hand. Several features from test hand images are extracted and stored in the database, which are used to train an artificial neural network (ANN). To facilitate easy usage of the hand geometry verification system (peg-free), a GUI was developed using MATLAB software. The developed system was validated and the overall result shows that the system can be used for biometric verification using hand geometry where the orientation and placement of the hand are not a necessity. The results show that the developed system performed better with a relatively low false acceptance rate and false rejection rate of 0.01% and 0.02% respectively. The system also has a lower mean square error of 8.84×10^-5.

References

Dutagaci, H., Sankur, B. and Yörük, E., 2008. Comparative analysis of global hand appearance-based person recognition. Journal of Electronic Imaging, 17(1), p.011018. DOI: https://doi.org/10.1117/1.2890986

Yachongka, V., Yagi, H. and Oohama, Y., 2021. Biometric identification systems with noisy enrollment for gaussian sources and channels. Entropy, 23(8), p.1049. DOI: https://doi.org/10.3390/e23081049

Wang, M., Hu, J. and Abbass, H.A., 2020. BrainPrint: EEG biometric identification based on analyzing brain connectivity graphs. Pattern Recognition, 105, p.107381. DOI: https://doi.org/10.1016/j.patcog.2020.107381

Kortli, Y., Jridi, M., Al Falou, A. and Atri, M., 2020. Face recognition systems: A survey. Sensors, 20(2), p.342. DOI: https://doi.org/10.3390/s20020342

Sepas‐Moghaddam, A., Pereira, F.M. and Correia, P.L., 2020. Face recognition: a novel multi‐level taxonomy based survey. IET Biometrics, 9(2), pp.58-67. DOI: https://doi.org/10.1049/iet-bmt.2019.0001

del Rio, J.S., Moctezuma, D., Conde, C., de Diego, I.M. and Cabello, E., 2016. Automated border control e-gates and facial recognition systems. Computers & Security, 62, pp.49-72. DOI: https://doi.org/10.1016/j.cose.2016.07.001

De Marsico, M., Nappi, M., Riccio, D. and Tortora, G., 2013. Entropy-based template analysis in face biometric identification systems. Signal, Image and Video Processing, 7(3), pp.493-505. DOI: https://doi.org/10.1007/s11760-013-0451-4

Nguyen, K., Fookes, C., Jillela, R., Sridharan, S. and Ross, A., 2017. Long range iris recognition: A survey. Pattern Recognition, 72, pp.123-143. DOI: https://doi.org/10.1016/j.patcog.2017.05.021

Salve, S.S. and Narote, S.P. 2016. Iris recognition using SVM and ANN. In Proceedings of the IEEE International Conference on Wireless Communications, Signal Processing and Networking, 23-25 March, Chennai, India. pp. 474-478. DOI: https://doi.org/10.1109/WiSPNET.2016.7566179

Hezil, H., Djemili, R. and Bourouba, H., 2018. Signature recognition using binary features and KNN. International Journal of Biometrics, 10(1), pp.1-15. DOI: https://doi.org/10.1504/IJBM.2018.090121

Piciucco, E., Maiorana, E. and Campisi, P., 2018. Palm vein recognition using a high dynamic range approach. IET Biometrics, 7(5), pp.439-446. DOI: https://doi.org/10.1049/iet-bmt.2017.0192

Abed, M.H., 2017. Wrist and Palm Vein pattern recognition using Gabor filter. Journal of AL-Qadisiyah for Computer Science and Mathematics, 9(1), pp.49-60. DOI: https://doi.org/10.29304/jqcm.2017.9.2.144

Funada, J.I., Ohta, N., Mizoguchi, M., Temma, T., Nakanishi, K., Murai, A., Sugiuchi, T., Wakabayashi, T. and Yamada, Y., 1998, August. Feature extraction method for palmprint considering elimination of creases. In Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No. 98EX170) (Vol. 2, pp. 1849-1854). IEEE.

Sancen-Plaza, A., Contreras-Medina, L.M., Barranco-Gutiérrez, A.I., Villaseñor-Mora, C., Martínez-Nolasco, J.J. and Padilla-Medina, J.A., 2020. Facial recognition for drunk people using thermal imaging. Mathematical Problems in Engineering, 2020, Article ID 1024173. DOI: https://doi.org/10.1155/2020/1024173

Abozaid, A., Haggag, A., Kasban, H. and Eltokhy, M., 2019. Multimodal biometric scheme for human authentication technique based on voice and face recognition fusion. Multimedia Tools and Applications, 78(12), pp.16345-16361. DOI: https://doi.org/10.1007/s11042-018-7012-3

Yoruk, E., Konukoglu, E., Sankur, B. and Darbon, J., 2006. Shape-based hand recognition. IEEE Transactions on Image Processing, 15(7), pp.1803-1815. DOI: https://doi.org/10.1109/TIP.2006.873439

Mostayed, A., Kabir, M.E., Khan, S.Z. and Mazumder, M.M.G., 2009, December. Biometric authentication from low resolution hand images using radon transform. In 2009 12th International Conference on Computers and Information Technology (pp. 587-592). IEEE. DOI: https://doi.org/10.1109/ICCIT.2009.5407305

Hussein, N.M.S., Hammed, S.M., and Ergen, B. 2017. Biometric identification system based on hand geometry. International Journal of Innovative Research in Science, 6(3), pp. 3159–3166.

Angadi, S. and Hatture, S., 2018. Hand geometry based user identification using minimal edge connected hand image graph. IET Computer Vision, 12(5), pp.744-752. DOI: https://doi.org/10.1049/iet-cvi.2017.0053

Taher, M.M. and George, L.E., 2022a. A digital signature system based on hand geometry-Survey. Wasit Journal of Computer and Mathematic Science, 1(1), pp. 1-14. DOI: https://doi.org/10.31185/wjcm.Vol1.Iss1.18

Taher, M.M. and George, L.E., 2022. A digital signature system based on hand geometry. Journal of Algebraic Statistics, 13(3), pp.4538-4556.

Mohammed, H.H., Baker, S.A. and Nori, A.S., 2021, February. Biometric identity Authentication System Using Hand Geometry Measurements. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012144). IOP Publishing. DOI: https://doi.org/10.1088/1742-6596/1804/1/012144

Dvořák, M., Drahanský, M. and Abdulla, W.H., 2021. On the fly biometric identification system using hand‐geometry. IET Biometrics, 10(3), pp.315-325. DOI: https://doi.org/10.1049/bme2.12024

Ghanbari, S., Ashtyani, Z.P. and Masouleh, M.T., 2022, May. User Identification Based on Hand Geometrical Biometrics Using Media-Pipe. In 2022 30th International Conference on Electrical Engineering (ICEE) (pp. 373-378). IEEE. DOI: https://doi.org/10.1109/ICEE55646.2022.9827056

Fang, L., Liang, N., Kang, W., Wang, Z. and Feng, D.D., 2020. Real-time hand posture recognition using hand geometric features and fisher vector. Signal Processing: Image Communication, 82, p.115729. DOI: https://doi.org/10.1016/j.image.2019.115729

Masood, D. and Qi, J., 2022. 3D Localization of Hand Acupoints Using Hand Geometry and Landmark Points Based on RGB-D CNN Fusion. Annals of Biomedical Engineering, pp.1-13. DOI: https://doi.org/10.1007/s10439-022-02986-1

Boreki, G. and Zimmer, A., 2005, October. Hand geometry: a new approach for feature extraction. In Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) (pp. 149-154). IEEE.

Adán, M., Adán, A., Vázquez, A.S. and Torres, R., 2008. Biometric verification/identification based on hands natural layout. Image and Vision Computing, 26(4), pp.451-465. DOI: https://doi.org/10.1016/j.imavis.2007.08.010

Villegas, O.O.V., Domínguez, H.D.J.O., Sánchez, V.G.C., Maynez, L.O. and Orozco, H.M., 2006. Biometric human identification of hand geometry features using discrete wavelet transform. Discrete Wavelet Transforms–Biomedical Applications, pp.251-266.

Bulatov, Y., Jambawalikar, S., Kumar, P. and Sethia, S., 2004, July. Hand recognition using geometric classifiers. In International Conference on Biometric Authentication (pp. 753-759). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-540-25948-0_102

Dhole, S.A. and Patil, V.H., 2012. Person identification using peg free hand geometry measurement. International Journal of Engineering Science and Technology, 4(6), pp.2943-2949.

Martinez, F., Orrite, C. and Herrero, E., 2005, June. Biometric hand recognition using neural networks. In International Work-Conference on Artificial Neural Networks (pp. 1164-1171). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/11494669_143

Kanhangad, V., Kumar, A. and Zhang, D., 2009, June. Combining 2D and 3D hand geometry features for biometric verification. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 39-44). IEEE. DOI: https://doi.org/10.1109/CVPRW.2009.5204306

Polat, Ö. and Yıldırım, T., 2008. Hand geometry identification without feature extraction by general regression neural network. Expert systems with Applications, 34(2), pp.845-849. DOI: https://doi.org/10.1016/j.eswa.2006.10.032

Klonowski, M., Plata, M. and Syga, P., 2018. User authorization based on hand geometry without special equipment. Pattern Recognition, 73, pp.189-201. DOI: https://doi.org/10.1016/j.patcog.2017.08.017

Iula, A., 2021. Biometric recognition through 3D ultrasound hand geometry. Ultrasonics, 111, p.106326. DOI: https://doi.org/10.1016/j.ultras.2020.106326

Iula, A. and Micucci, M., 2022. Multimodal Biometric Recognition Based on 3D Ultrasound Palmprint-Hand Geometry Fusion. IEEE Access, 10, pp.7914-7925. DOI: https://doi.org/10.1109/ACCESS.2022.3143433

Bača, M., Grd, P. and Fotak, T., 2012. Basic principles and trends in hand geometry and hand shape biometrics. New Trends and Developments in Biometrics, pp.77-99. DOI: https://doi.org/10.5772/51912

Damousis, I.G. and Argyropoulos, S., 2012. Four machine learning algorithms for biometrics fusion: A comparative study. Applied Computational Intelligence and Soft Computing, 2012. DOI: https://doi.org/10.1155/2012/242401