Person Authentication Based on the Difference of Deep Features Extracted from the Ocular and Face Regions
Resumo
This paper presents a new method for person authentication that relies on the fusion of two biometric authentication methods based, respectively, on ocular deep features and facial deep features. In our work, the deep features are extracted from the regions of interest by using a very deep CNN (Convolutional Neural Network). Another interesting aspect of our work is that, instead of using directly the deep features as input for the authentication methods, we use the difference between the probe and gallery deep features. So, our method adopts a pairwise strategy. Support Vector Machine classifiers are trained separately for each approach. The fusion of the ocular and the facial based methods are carried out in the score level. The proposed method was assessed with a facial database taken under uncontrolled environment and reached good results. Besides, the fusion strategy proposed in this work showed better results than the results obtained by each individual method.
Palavras-chave:
Person Authentication, Ocular Recognition, Face Recognition, Multibiometrics, Deep Learning, Convolutional Neural Networks
Referências
A. K. Jain, A. A. Ross, and K. Nandakumar, Introduction, in Introduction to Biometrics, pp. 1 - 49, Springer, DOI: 10.1007/978-0-387-77326-1_1
S. Pankanti, R. M. Bolle, and A. Jain, Biometrics: The future of identification [guest eeditors ' introduction], Computer, vol. 33, no. 2, pp. 46 - 49, DOI: 10.1109/2.820038
G. Santos and H. Proença, Periocular biometrics: An emerging technology for unconstrained scenarios, in Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013 IEEE Workshop on, pp. 14 - 21, IEEE, 2013.
F. Alonso-Fernandez and J. Bigun, Periocular biometrics: databases, algorithms and directions, in 2016 4th International Conference on Biometrics and Forensics (IWBF), pp. 1 - 6, March DOI: 10.1109/iwbf.2016.7449688
I. Nigam, M. Vatsa, and R. Singh, Ocular biometrics: A survey of modalities and fusion approaches, Information Fusion, vol. 26, pp. 1 - 35, DOI: 10.1016/j.inffus.2015.03.005
O. M. Parkhi, A. Vedaldi, and A. Zisserman, Deep face recognition, in British Machine Vision Conference, DOI: 10.5244/c.29.41
C. N. Padole and H. Proenca, Periocular recognition: Analysis of performance degradation factors, in 2012 5th IAPR International Conference on Biometrics (ICB), pp. 439 - 445, March DOI: 10.1109/icb.2012.6199790
L. Deng and Y. Dong, Deep learning: methods and applications, Foundations and Trends in Signal Processing, vol. 7, no. 3-4, pp. 197 - 387, DOI: 10.1561/2000000039
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Anais da IEEE, vol. 86, no. 11, pp. 2278 - 2324, DOI: 10.1109/5.726791
R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ' 14, (Washington, DC, USA), pp. 580 - 587, IEEE Computer Society.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1 - 9, June.
Y. Sun, X. Wang, and X. Tang, Deep learning face representation from predicting 10 ,000 classes, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ' 14, (Washington, DC, USA), pp. 1891 - 1898, IEEE Computer Society.
W. Wang, J. Yang, J. Xiao, S. Li, and D. Zhou, Face recognition based on deep learning, in International Conference on Human Centered Computing, pp. 812 - 820, Springer, DOI: 10.1007/978-3-319-15554-8_73
A. Meraoumia, F. Kadri, H. Bendjenna, S. Chitroub, and A. Bouridane, Improving biometric identification performance using pcanet deep learning and multispectral palmprint, in Biometric Security and Privacy, pp. 51 - 69, Springer.
X. Sun, P. Wu, and S. C. Hoi, Face detection using deep learning: An improved faster rcnn approach, arXiv preprint arXiv: 08289, 2017. DOI: 10.1016/j.neucom.2018.03.030
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, Deepface: Closing the gap to human-level performance in face verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701 - 1708, June.
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556
S. Minaee, A. Abdolrashidiy, and Y. Wang, An experimental study of deep convolutional features for iris recognition, in 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1 - 6, Dec 2016.
N. Ballas, L. Yao, C. Pal, and A. Courville, Delving deeper into convolutional networks for learning video representations, arXiv preprint arXiv:1511.06432
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499 - 1503, DOI: 10.1109/lsp.2016.2603342
P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, Overview of the face recognition grand challenge, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 947 - 954 vol. 1, June 2005.
N. Guenther, M. Schonlau, et al., Support vector machines, Stata J, vol. 16, no. 4, pp. 917 - 937, DOI: 10.1177/1536867x1601600407
T. Panchal and A. Singh, Multimodal biometric system, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 5, 2013.
A. Ross and A. Jain, Information fusion in biometrics, Pattern recognition letters, vol. 24, no. 13, pp. 2115 - 2125, DOI: 10.1016/s0167-8655(03)00079-5
B. Chinmay, B. Rupesh, B. Nikhil, and R. Milind, Face identification. IJESC 2017, Research article volume 7, No 5, 2017.
C. Ding, J. Choi, D. Tao, and L. S. Davis, Multi-directional multi-level dual-cross patterns for robust face recognition, IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 3, pp. 518 - 531, 2015.
S. Pankanti, R. M. Bolle, and A. Jain, Biometrics: The future of identification [guest eeditors ' introduction], Computer, vol. 33, no. 2, pp. 46 - 49, DOI: 10.1109/2.820038
G. Santos and H. Proença, Periocular biometrics: An emerging technology for unconstrained scenarios, in Computational Intelligence in Biometrics and Identity Management (CIBIM), 2013 IEEE Workshop on, pp. 14 - 21, IEEE, 2013.
F. Alonso-Fernandez and J. Bigun, Periocular biometrics: databases, algorithms and directions, in 2016 4th International Conference on Biometrics and Forensics (IWBF), pp. 1 - 6, March DOI: 10.1109/iwbf.2016.7449688
I. Nigam, M. Vatsa, and R. Singh, Ocular biometrics: A survey of modalities and fusion approaches, Information Fusion, vol. 26, pp. 1 - 35, DOI: 10.1016/j.inffus.2015.03.005
O. M. Parkhi, A. Vedaldi, and A. Zisserman, Deep face recognition, in British Machine Vision Conference, DOI: 10.5244/c.29.41
C. N. Padole and H. Proenca, Periocular recognition: Analysis of performance degradation factors, in 2012 5th IAPR International Conference on Biometrics (ICB), pp. 439 - 445, March DOI: 10.1109/icb.2012.6199790
L. Deng and Y. Dong, Deep learning: methods and applications, Foundations and Trends in Signal Processing, vol. 7, no. 3-4, pp. 197 - 387, DOI: 10.1561/2000000039
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Anais da IEEE, vol. 86, no. 11, pp. 2278 - 2324, DOI: 10.1109/5.726791
R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ' 14, (Washington, DC, USA), pp. 580 - 587, IEEE Computer Society.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1 - 9, June.
Y. Sun, X. Wang, and X. Tang, Deep learning face representation from predicting 10 ,000 classes, in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ' 14, (Washington, DC, USA), pp. 1891 - 1898, IEEE Computer Society.
W. Wang, J. Yang, J. Xiao, S. Li, and D. Zhou, Face recognition based on deep learning, in International Conference on Human Centered Computing, pp. 812 - 820, Springer, DOI: 10.1007/978-3-319-15554-8_73
A. Meraoumia, F. Kadri, H. Bendjenna, S. Chitroub, and A. Bouridane, Improving biometric identification performance using pcanet deep learning and multispectral palmprint, in Biometric Security and Privacy, pp. 51 - 69, Springer.
X. Sun, P. Wu, and S. C. Hoi, Face detection using deep learning: An improved faster rcnn approach, arXiv preprint arXiv: 08289, 2017. DOI: 10.1016/j.neucom.2018.03.030
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, Deepface: Closing the gap to human-level performance in face verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701 - 1708, June.
K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556
S. Minaee, A. Abdolrashidiy, and Y. Wang, An experimental study of deep convolutional features for iris recognition, in 2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1 - 6, Dec 2016.
N. Ballas, L. Yao, C. Pal, and A. Courville, Delving deeper into convolutional networks for learning video representations, arXiv preprint arXiv:1511.06432
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499 - 1503, DOI: 10.1109/lsp.2016.2603342
P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, Overview of the face recognition grand challenge, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 947 - 954 vol. 1, June 2005.
N. Guenther, M. Schonlau, et al., Support vector machines, Stata J, vol. 16, no. 4, pp. 917 - 937, DOI: 10.1177/1536867x1601600407
T. Panchal and A. Singh, Multimodal biometric system, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 5, 2013.
A. Ross and A. Jain, Information fusion in biometrics, Pattern recognition letters, vol. 24, no. 13, pp. 2115 - 2125, DOI: 10.1016/s0167-8655(03)00079-5
B. Chinmay, B. Rupesh, B. Nikhil, and R. Milind, Face identification. IJESC 2017, Research article volume 7, No 5, 2017.
C. Ding, J. Choi, D. Tao, and L. S. Davis, Multi-directional multi-level dual-cross patterns for robust face recognition, IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 3, pp. 518 - 531, 2015.
Publicado
09/09/2019
Como Citar
VIZONI, Marcelo Vilela; MARANA, Aparecido Nilceu.
Person Authentication Based on the Difference of Deep Features Extracted from the Ocular and Face Regions. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 15. , 2019, São Bernardo do Campo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 55-60.
DOI: https://doi.org/10.5753/wvc.2019.7628.
