Title: IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION
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Title: IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION
IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION
Michaela Chudobova; Jan Kubicek; Radomir Scurek; Marek Hutter
10.5593/sgem2022/2.1/s07.06
10.5593/sgem2022/2.1
1314-2704
978-619-7603-40-8
English
22
2.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Informatics
This article deals with the design and implementation of an intelligent biometric system that allows the detection and classification of a person's face from static image data and creates a system for evaluating its reliability. In its introductory part, it theoretically describes applied biometrics and biometric systems for security identification and user verification, and also deals with the theory of the description of algorithms for human face detection and recognition. Subsequently, the authors use the MATLAB programming language, which is highly optimized for modern processors and memory architectures, to focus on the implementation and testing of a biometric system using Viola-Jones algorithms and a convolutional neural network with a pre-trained network NetNet. Convolutional neural networks (CNN) are the most recognized and popular deep-learning neural networks, which are based on layers that perform two-dimensional (2D) convolution of input data with learned filters. In the final part there is a discussion where, based on the results of testing, the robustness and efficiency of the proposed intelligent biometric system is objectively evaluated. The results allow for the continued development of other pre-trained artificial neural networks, variable implementations for facial recognition, but also other things, such as the recognition of potentially dangerous people.
biometric system, MATLAB, face detection, convolutional neural network, cybersecurity
[1] Drahansky, M. Modern biometric systems based on more characteristics and their properties: thesis of the lecture on the professorship procedure in the field of Computer Science and Informatics. Brno: Brno University of Technology, VUTIUM publishing house (in Czech), 2016. ISBN 978-80-214-5451-4.
[2] Jain, A., Anil K., Arun A. Nandakumar, R., Nandakumar, K., Introduction to biometrics. New York: Springer, c2011. ISBN 978-0-387-77325-4.
[3] Uhl, A., Busch Ch., Marcel S., Veldhuis R., Handbook of vascular biometrics. Cham: Springer Open, 2020. Advances in computer vision and pattern recognition. ISBN 978-0-387-77325-4.
[4] Shaheed, K., Mao, A., Qureshi, I., A Systematic Review on Physiological-Based Biometric Recognition Systems: Current and Future Trends. Archives of Computational Methods in Engineering [online]. 2021 [cit. 2021-7-16]. Available from: doi:https://doi.org/10.1007/s11831-021-09560-3
[5] Abdullah, I., Stephan, J., A Survey of Face Recognition Systems. Ibn Al Haitham Journal for Pure and Applied Science [online]. 2021, 34(2), 144-160 [cit. 2021-7-16]. Available from: 10.30526/34.2.2620
[6] Sulovska, K., Biometric systems focused on face recognition, their reliability and basic methods for their creation. POSTERUS: Portal for professional publishing [online] (in Czech). 2011, 4(9) [cit. 2021-7-16]. ISSN 1338-0087. Available from: http://www.posterus.sk/?p=11511
[7] Drahansky, M., Orsag, F., Biometry (in Czech). [Brno: M. Drahansky], 2011. ISBN 978-80-254-8979-6.
[8] Kostrli, Y., Jridi, M., Al Falou, A., Atri, M., Face Recognition Systems: A Survey. Sensors (Basel) [online]. 20(2) [cit. 2021-7-16].
[9] Meenpal, T., Goyal, A., Face Recognition System based on Principal Components Analysis and Distance Measures. International Journal of Engineering Technologies IJET, 2021 [online]. [cit. 2021-7-16].
[10] Jirkovsky, J., Deep Learning methods for image segmentation (in czech) [online]. 2017 [cit. 2021-7-16]. Available from: http://automa.cz/Aton/FileRepository/pdf_articles/11149.pdf
[11] Gonzalez, R., C., Woods, R., E., Eddins, S., L., Digital image processing using MATLAB. 2nd ed.: Gatesmark Publishing, c2009. ISBN 978-0-9820854-0-0.
[12] FEI Face Database [online]. Centro Universitario da FEI, Sao Bernardo do Campo, Sao Paulo, Brazil [cit. 2022-01-06]. Available from: https://fei.edu.br/~cet/facedatabase.html
conference
https://doi.org/10.5593/sgem2022/2.1/s07.06
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Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 04 - 10 July, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
43-50
04 - 10 July, 2022
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