Deep Learning Utilized for Person Recognition Based on the Biometric Features of the Periocular Region
Resumo
This article proposes the use of deep learning technologies to perform visual biometric recognition. The results obtained by convolutional neural networks trained to perform multi-class classification based on the visual features of the human periocular region are presented and discussed, in addition to being compared with results obtained using pattern recognition for biometric recognition from human iris textures.
Palavras-chave:
Biometry, Artificial Intelligence, Computer Vision, Deep Learning, Periocular Region
Referências
Vielhauer, Claus. Biometric User Authentication for IT Security: From Fundamentals to Handwriting. 2006th ed, Springer, 2005.
Park, U; Ross, A.; Jain, A. K. Periocular biometrics in the visible spectrum: A feasibility study. 2009. IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems. pp. 1-6.
C. Rathgeb and C. Busch, Eds. Iris and Periocular Biometric Recognition. Stevenage, England: Institution of Engineering and Technology, 2017.
R. M. Bodade and S. Talbar, Iris Analysis for Biometric Recognition Systems, 2014th ed. New Delhi, India: Springer, 2014.
Khan, S., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Morgan Claypool.
CASIA-IrisV4. Chinese Academy of Sciences Institute of Automation. http://biometrics.idealteste.org Tech. Rep. 2005.
PROENç A, H. et al. The UBIRIS.v2: A Database of Visible Wavelength Imagens captured On-The-Move and At-A-Distance. IEEE Trans. PAMI, 2010. v. 32, n. 8, p. 1529-1536. 2010.
Howard, Andrew G. et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. ArXiv abs/1704.04861. 2017.
Szegedy, Christian et al. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.
P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001, pp. I-I, doi: 10.1109/CVPR.2001.990517
Souza, Jones & Gonzaga, Adilson. (2019). Human iris feature extraction under pupil size variation using local texture descriptors. Multimedia Tools and Applications. 78. 10.1007/s11042-019-7371-4.
Park, U; Ross, A.; Jain, A. K. Periocular biometrics in the visible spectrum: A feasibility study. 2009. IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems. pp. 1-6.
C. Rathgeb and C. Busch, Eds. Iris and Periocular Biometric Recognition. Stevenage, England: Institution of Engineering and Technology, 2017.
R. M. Bodade and S. Talbar, Iris Analysis for Biometric Recognition Systems, 2014th ed. New Delhi, India: Springer, 2014.
Khan, S., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Morgan Claypool.
CASIA-IrisV4. Chinese Academy of Sciences Institute of Automation. http://biometrics.idealteste.org Tech. Rep. 2005.
PROENç A, H. et al. The UBIRIS.v2: A Database of Visible Wavelength Imagens captured On-The-Move and At-A-Distance. IEEE Trans. PAMI, 2010. v. 32, n. 8, p. 1529-1536. 2010.
Howard, Andrew G. et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. ArXiv abs/1704.04861. 2017.
Szegedy, Christian et al. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.
P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001, pp. I-I, doi: 10.1109/CVPR.2001.990517
Souza, Jones & Gonzaga, Adilson. (2019). Human iris feature extraction under pupil size variation using local texture descriptors. Multimedia Tools and Applications. 78. 10.1007/s11042-019-7371-4.
Publicado
24/10/2022
Como Citar
NASCIMENO, Leonardo Ferreira; SOUZA, Jones Mendonça de.
Deep Learning Utilized for Person Recognition Based on the Biometric Features of the Periocular Region. In: WORKSHOP DE TRABALHOS DA GRADUAÇÃO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 101-104.
DOI: https://doi.org/10.5753/sibgrapi.est.2022.23270.
