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Abstrakty
Results of investigation of face detection algorithms efficiency in the banking client visual verification system are presented. The video recordings were made in real conditions met in three bank operating outlets employing a miniature industrial USB camera. The aim of the experiments was to check the practical usability of the face detection method in the biometric bank client verification system. The main assumption was to provide a simplified as much as possible user interaction with the application. Applied algorithms for face detection are described and achieved results of face detection in the real bank environment conditions are presented. Practical limitations of the application based on encountered problems are discussed.
Słowa kluczowe
Rocznik
Tom
Strony
137--148
Opis fizyczny
Bibliogr. 12 poz., fig., tab.
Twórcy
autor
- Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk
autor
- Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdańsk
Bibliografia
- [1] Dalka P., Bratoszewski P., Czyzewski A., Visual lip contour detection for the purpose of speech recognition, 2014 International Conference on Signals and Electronic System (ICSES), Poland, 2014.
- [2] Górski G., System płatności mobilnych wykorzystujący biometryczną identyfikację użytkowników oraz infrastrukturę klucza publicznego, PRZEGLĄD TELEKOMUNIKACYJNY 8-9/2015 (in Polish).
- [3] Hadid A., Heikkila J.Y., Silven O., Pietikainen M., Face and eye detection for person authentication in mobile phones, 2007 First ACM/IEEE International Conference on Distributed Smart Cameras, Vienna, 2007, pp. 101-108.
- [4] Laganiere R., OpenCV Computer Vision Application Programming Cookbook, Packt Publishing Ltd., 2014.
- [5] Lupu C., Gaitan V.G., Lupu V., Security enhancement of internet banking applications by using multimodal biometrics, IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2015, Herl'any, 2015, pp. 47-52.
- [6] Marciniak T., Chmielewska A., Weychan R., Parzych M., Dąbrowski A., Influence of low resolution of images on reliability of face detection and recognition, Multimedia Tools and Applications, 74, 12, 4329–4349, 2015.
- [7] Mariano V.Y. et al., Performance evaluation of object detection algorithms, 16th International Conference on Pattern Recognition, 2002. Proceedings, vol. 3, pp. 965-969, 2002.
- [8] Ren J., Kehtarnavaz N., Estevez L., Real-time optimization of Viola-Jones face detection for mobile platforms, Circuits and Systems Workshop: System-on-Chip - Design, Applications, Integration, and Software, 2008 IEEE Dallas, Dallas, TX, 2008, pp. 1-4.
- [9] Szczodrak M., Czyżewski A., Face detection algorithms evaluation for the bank client verification; Proc. Signal Processing, Algorithms, Architectures, Arrangements, and Applications, pp. 186-190, Poznań, Polska, 21.9.2016 - 23.9.2016.
- [10]Taigman Y., Yang M., Ranzato M., Wolf L., DeepFace: closing the gap to human-level performance in face verification, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1701-1708.
- [11]Viola P., Jones M., Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001. Vol. 1. IEEE, 2001.
- [12]Vondrick C., Patterson D., Ramanan D., Efficiently scaling up crowdsourced video annotation, International Journal of Computer Vision (IJCV). June 2012.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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