The new hand geometry system and automatic identification

Shihab A. Shawkat, Khalid Saeed Lateef Al-badri, Ahmed Ibrahim Turki


For the past decades an extensive amount of time and effort has been consumed for the research and development of biometric-based recognition systems. One such system is the one that can recognize based on hand geometry. The objective of this thesis is to explore the usage of hand geometry for developing a hand geometry recognition system. This paper proposes a system performing automatic recognition without the use of specific hardware. The system emphases on executing feature extractions from a typical database and then developing a neural network classifier based on back-propagation architectures with various exercise methods. Features are dug out by the use of morphological (segmentation) operation. Our Experiments were carried out on 500 images (50 persons, 10 images each) under distinctive conditions with possible deliberation of scaling, Translation, Rotation, Color and Illumination modification. The accurate recognition rate is about 96.41 % for the matching of artificial neural network which is calculated by the formula average of sum of errors divided over the number of images.


Biometrics System Hand Geometry Human Biometric Identification

Full Text:



D. Bhattacharyya, R. Ranjan, F. Alisherov, and M. Choi, “Biometric authentication: A review,” Int. J. u-and e-Service, Sci. Technol., vol. 2, no. 3, pp. 13–28, 2009.

M. Bhatnagar, R. K. Jain, and N. S. Khairnar, “A Survey on Behavioral Biometric Techniques: Mouse vs Keyboard Dynamics,” Int. J. Comput. Appl., vol. 975, p. 8887, 2013.

A. Ross and A. K. Jain, “Human recognition using biometrics: an overview,” in Annales Des Télécommunications, 2007, vol. 62, no. 1–2, pp. 11–35.

R. Katiyar, V. K. Pathak, and K. V Arya, “A study on existing gait biometrics approaches and challenges,” Int. J. Comput. Sci. Issues, vol. 10, no. 1, p. 135, 2013.

V. Sravya, P. K. Murthy, R. B. Kallam, and B. Srujana, “A surey on fingerprint biometric system,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 2, no. 4, 2012.

U. Chaudhary, S. Bhardwaj, and H. Sabharwal, “Fingerprint Recognition using orientation features,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 4, no. 5, 2014.

S. Ravi and P. M. Dattatreya, “A Study of Biometric Approach Using Finger Print Recognition,” Notes Softw. Eng., vol. 1, no. 2, 2013.

V. Štruc and N. Pavešić, Hand-geometry device. Springer, 2015.

K. Mali and S. Bhattacharya, “Comparative study of different biometric features,” Int. J. Adv. Res. Comput. Commun. Eng., vol. 2, no. 7, p. 8, 2013.

V. Dhir, A. S. Acet, R. Kumar, and G. Singh, “Biometric recognition: A modern era for security,” Int. J. Eng. Sci. Technol., vol. 2, no. 8, pp. 3364–3380, 2010.

V. Conti, C. Militello, and S. Vitabile, “Biometric authentication overview: a fingerprint recognition sensor description,” Int J Biosen Bioelectron, vol. 2, no. 1, pp. 26–31, 2017.

S. J. Elliott, B. Senjaya, E. P. Kukula, J. M. Werner, and M. Wade, “An evaluation of the human biometric sensor interaction using hand geometry,” in 44th Annual 2010 IEEE International Carnahan Conference on Security Technology, 2010, pp. 259–265.

R. Kannavara and K. L. Shippy, “Topics in biometric human-machine interaction security,” IEEE Potentials, vol. 32, no. 6, pp. 18–25, 2013.

S. P. Priyal and P. K. Bora, “A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments,” Pattern Recognit., vol. 46, no. 8, pp. 2202–2219, 2013.

I. Putra and M. A. Sentosa, “Hand geometry verification based on chain code and dynamic time warping,” Int. J. Comput. Appl., vol. 38, no. 12, pp. 17–22, 2012.

A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. circuits Syst. video Technol., vol. 14, no. 1, 2004.

Y. Cui and J. Weng, “A learning-based prediction-and-verification segmentation scheme for hand sign image sequence,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 8, pp. 798–804, 1999.

A. Ramamoorthy, N. Vaswani, S. Chaudhury, and S. Banerjee, “Recognition of dynamic hand gestures,” Pattern Recognit., vol. 36, no. 9, pp. 2069–2081, 2003.

N. Liu and B. C. Lovell, “Hand gesture extraction by active shape models,” in Digital Image Computing: Techniques and Applications (DICTA’05), 2005, p. 10.

K. Karthik, K. Varalakshmi, and S. Ravi, “A file authentication system using hand gesture passcodes,” Int. J. Emerg. Technol. Comput. Appl. Sci., vol. 13, no. 174, pp. 394–401, 2013.

M. Inalpolat and Durakovic, B., “Implementation of Advanced Automated Material Handling Systems in Manufacturing Environment”, European Conference of Technology and Society - EuroTecS. 2013.

O. Ayurzana, B. Pumbuurei, and H. Kim, “A study of hand-geometry recognition system,” in Ifost, 2013, vol. 2, pp. 132–135.

B. Durakovic, "Design of Experiments Application, Concepts, Examples: State of the Art," Periodicals of Engineering and Natural Scinces, vol. 5, no. 3, p. 421‒439, 2017.

D. R. Chaudhary and A. Sharma, “Hand geometry based recognition system,” in 2012 Nirma University International Conference on Engineering (NUiCONE), 2012, pp. 1–5.

A. Giełczyk, M. Choraś, and R. Kozik, “Hybrid Feature Extraction for Palmprint-Based User Authentication,” in 2018 International Conference on High Performance Computing & Simulation (HPCS), 2018, pp. 629–633.

H. Imtiaz and S. A. Fattah, “A histogram-based dominant wavelet domain feature selection algorithm for palm-print recognition,” Comput. Electr. Eng., vol. 39, no. 4, pp. 1114–1128, 2013.

S. A. Shawkat, O. Abu-Elnasr, and T. Elarif, “Evolved Algorithm to Secure Communication with Steganography”, International Journal of Intelligent Computing and Information ‎Science (IJICIS)‎, 2017, vol. 17, , no. 1, pp. 1–17.

A. Kumar and D. Zhang, “Personal recognition using hand shape and texture,” IEEE Trans. image Process., vol. 15, no. 8, pp. 2454–2461, 2006.



  • There are currently no refbacks.

Copyright (c) 2019 Shihab A. Shawkat, Khalid Saeed Lateef Al-badri, Ahmed Ibrahim Turki

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License