A digital signature system based on hand geometry - Survey
Basic Components of Hand-based Biometric System
DOI:
https://doi.org/10.31185/wjcm.Vol1.Iss1.18Keywords:
Hand geometry, Biometric, Pattern recognition, Artificial neural networkAbstract
In recent years large number of emerging automated applications faces the need to have recognition abilities of persons using their own self biometrics, before they can access the applications services. Nowadays, Biometric recognition is used, it can be used as automatic identification or automatic verification of persons based on their physiological or behavioral characteristics. There are no perfect biometric measurements; each biometry has its advantages and limitations. Each biometry requires specific vital identity to answer the identification or verification question. The suitability of a particular biometry for a particular application depends on many factors. Hand geometry/shape is a very simple biometric technology that uses the measurements of human hand to verify the identity of the individuals. The measurements include the distance between certain mark points, shape and width of fingers and size of palm. The biometric systems that employing hand geometry become widely used since they have high public acceptance. This article aims to survey several articles found in literature about hand based biometric system, and to compare different methods of biometric recognition that based on hand geometry.
References
A. Babich, “Biometric authentication. Types of biometric identifiers,” Bachelors Thesis, HAAGA-HELIA University of Applied Sciences. Finland.2012.
B. Aghili and H. Sadjedi, “Personal authentication using hand geometry,” International Conference on Computational Intelligence and SoftwareEngineering, 2009.
M. Faundez-Zanuy, “Biometric verification of humans by means of hand geometry,” Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology, 2005.
A. Ross, K. Nandakumar, and A. K. Jain, Introduction to multibiometrics. Boston, MA: Springer, 0271.
R. Zunkel, “Hand geometry based verification,” BIOMETRICS: Personal Identification in Networked Society, 1998.
N. Saxena, “Hand geometry: A new method for biometric recognition,” International Journal of Soft Computing and Engineering (IJSCE), vol. 2, no. 6, pp. 2231–2307, 2013.
A. Dantcheva, P. Elia, and A. Ross, “What else does your biometric data reveal? A survey on soft biometrics,” IEEE Transactions on Information Forensics and Security, vol. 11, pp. 441–467, 2015.
X. Wu and Q. Zhao, “Deformed palmprint matching based on stable regions,” IEEE Transactions on Image Processing, vol. 24, pp. 4978–4989, 2015.
M. S. Velmurugan and S. Selvarajan, “Linear binary pattern based biometric recognition using hand geometry and iris images,” International Journal of Applied Engineering Research, vol. 10, pp. 45675–45683, 2015.
S. A. Angadi and S. M. Hatture, “User identification using wavelet features of hand geometry graph,” SAI Intelligent Systems Conference (IntelliSys), 2015.
M. S. Nashwan, Hussein, M. Sipan, B. Hammed, and Ergen, “Biometric Identification System based on Hand Geometry,” International Journal of Innovative Research in Science, Engineering and Technology ISSN, vol. 6, pp. 3159–3166, 2017. p. 1-9
Y. Song, Z. Cai, and Z.-L. Zhang, “Multi-touch authentication using hand geometry and behavioral information,” 2017 IEEE symposium on security and privacy (SP.
Abdullah, “Palm and hand geometry features hand recognition using,” International Journal of Scientific & Engineering Research, vol. 9, no. 4, 2018.
D. Lu, “Multifactor user authentication with in-air-handwriting and hand geometry,” 2018 International Conference on Biometrics (ICB), 2018.
M. Khaliluzzaman, M. Mahiuddin, and M. M. Islam, “Hand geometry based person verification system,” International Conference on Innovations in Science, Engineering and Technology (ICISET), 2018.
S. A. Shawkat, A. I. K. S. L. Al-Badri, and Turki, “The new hand geometry system and automatic identification,” Periodicals of Engineering and Natural Sciences (PEN), vol. 7, no. 3, pp. 996–1008, 2019.
K. Prihodova and M. Hub, “Hand-Based Biometric System Using Convolutional Neural Networks,” Acta Informatica Pragensia, vol. 9, pp. 48–57, 2020.
Al-Kateeb, N. Zeena, and S. J. Mohammed, “A novel approach for audio file encryption using hand geometry,” Multimedia Tools and Applications, vol. 79, pp. 19615–19628, 2020.
S. Mohmmad, R. Dadi, A. Harshavardhan, S. Haider, Y. Aqeel, S. M. Rehman, and Ali, “Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics, 9,” 2020.
A. Malik and S. S. Dub, “ANN Hand Geometrics Feature Optimization Based Recognition Technique.”
L. Oldal, A. Gulyás, and Kovács, “Biometric Authentication System based on Hand Geometry and Palmprint Features.”
H. Mohammed, Hashim, A. Shatha, A. S. Baker, and Nori, “Biometric identity Authentication System Using Hand Geometry Measurements,” Journal of Physics: Conference Series, vol. 1804, no. 1, 2021.
R. Doroz, “A New Hand-Movement-Based Authentication Method Using Feature Importance Selection with the Hotelling’s Statistic,” Journal of Artificial Intelligence and Soft Computing Research, vol. 12, pp. 41–59, 2022.
M. Alam, M. T. Mahmudul, Islam, and S. M. Rahman, “Unified learning approach for egocentric hand gesture recognition and fingertip detection,” Pattern Recognition, vol. 121, 2022.
M. T. M. M. Alam, S. M. Islam, and M. Rahman, “Unified learning approach for egocentric hand gesture recognition and fingertip detection,” Pattern Recognition, vol. 121, pp. 31–3203, 2021.
S. J. Elliott, “An evaluation of the human biometric sensor interaction using hand geometry, IEEE,” 2010.
P. Antitza, A. Elia, and Ross, “What else does your biometric data reveal? A survey on soft biometrics,” IEEE Transactions on Information Forensics and Security, vol. 11, pp. 441–467, 2015.
H. Sim and Moi, “Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images,” Expert Systems with Applications, vol. 41, pp. 5390–5404, 2014.
Al-Fiky, M. Firas, and Z. S. Ageed, “A new features extracted for recognizing a hand geometry using BPNN,” International Journal of Scientific & Engineering Research, vol. 5, 2014.
A. Bera, D. Bhattacharjee, and M. Nasipuri, “Person recognition using alternative hand geometry,” International Journal of Biometrics, vol. 6, pp. 231–247, 2014.
X. Wu and Q. Zhao, “Deformed palmprint matching based on stable regions,” IEEE Transactions on Image Processing, vol. 24, pp. 4978–4989, 2015.
E. P. Kukula, “Defining habituation using hand geometry,” IEEE Workshop on Automatic Identification Advanced Technologies. IEEE, 2007.
I. ISO, IEC 19795-1: Information technology-biometric performance testing and reporting-part 1: Principles and framework, vol. 1. 2006.
U. Scherhag, C. Rathgeb, and C. Busch, “Morph deterction from single face image: A multi-algorithm fusion approach,” Proceedings of the 2018 2nd International Conference on Biometric Engineering and Applications, 2018.
M. Afifi, “11K Hands: Gender recognition and biometric identification using a large dataset of hand images,” Multimedia Tools and Applications, vol. 78, pp. 20835–20854, 2019.
W. Zuo, et al. “The multiscale competitive code via sparse representation for palmprint verification.” 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Mays M. Taher, Dr. Loay E. George
This work is licensed under a Creative Commons Attribution 4.0 International License.
