Recently, the facial recognition has aroused the interest of the scientific community, this technique of biometric that is effective, non-intrusive and contactless has taken an increasingly important part in the field of research. This paper proposes a face recognition and classification method based on deep learning, in particular Convolutional Neural Network (CNN), which are incredibly a powerful tools that have found great success in image classification and pattern recognition. In this work, the approach to this task is based on the Convolutional Neural Network (CNN) as a powerful feature extraction followed by Support Vector Machines (SVM) as a high classifier. To reduce the dimension of these features, a principal component analysis (PCA) technique is employed. We conduct an extensive evaluation of our methods on the FERET dataset. The results obtained showed that the proposed method CNN combine with PCA and Svc solution provide a significant improvement in performance and enhance the recognition accuracy.
biometrics, face recognition, feature extraction, convolutional neural network, CNN, support vector machines (SVM), SVC, principal component analysis, PCA
Ahonen T., Hadid A., Pietikainen M. (2004). Face recognition with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037-2041. https://doi.org/10.1007/978-3-540-24670-1_36
Andrews R., Diederich J., Tickle A. B. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based Systems, pp. 373-389. https://doi.org/10.1016/0950-7051(96)81920-4
Bengio Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, No. 1, pp. 43–44. https://doi.org/10.1561/2200000006
Ben-hur A., Horn D., Siegelmann H. T., Vapnik V. (2001). Support vector clustering. Journal of Machine Learning Research, Vol. 2, pp. 125-137. https://doi.org/10.1162/15324430260185565
Bledsoe W. W. (1968). Semiautomatic facial recognition. Technical Report Project 6693, Stanford Research Institute, Menlo Park, California.
Chowdhury A. R., Lin T. Y., Maji S., Learned-Miller E. (2016). One-to-many face recognition with bilinear CNNs. Applications of Computer Vision (WACV), pp. 1-9. https://doi.org/10.1109/WACV.2016.7477593
Ciresan D., Meier U., Masci J., Gambardella L. M., Schmidhuber J. (2013). High performance convolutional neural networks for image classification. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Vol. 2, pp. 1237–1242. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-210
Cootes T., Edwards G., Taylor C. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/34.927467
Cortes C., Vapnik V. (1997). Soft Margin Classifier. U.S. Patent No. 5, 640,492.
Dong Y., Zhen L., Shengcai L., Stan Z. L. (2014). Learning face representation from scratch. arXiv preprint arXiv: 1411.7923.
Fukushima K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, Vol. 36, pp. 193–202. https://doi.org/10.1007/BF00344251
Guo G., Li S., Chan K. (2000). Face recognition by support vector machines. The IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196-201. https://doi.org/10.1109/AFGR.2000.840634
Guo S., Chen S., Li Y. (2016). Face recognition based on convolutional neural network and support vector machine. Proceedings of the IEEE International Conference on Information and Automation Ningbo, China. https://doi.org/ 10.1109/ICInfA.2016.7832107
Hinton G. E., Osindero S., Teh Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, Vol. 8, No. 7, pp. 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
Khiyari H. E., Wechsler H. (2016). Face recognition across time lapse using convolutional neural networks. Journal of Information Security, No. 7, pp. 141-151.
Kirby M., Sirovich L. (1990). Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, pp. 103-108. https://doi.org/10.1109/34.41390
Kriegman D. J., Hespanha J. P., Belhumeur P. N. (1996). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. In ECCV. https://doi.org/10.1109/34.598228
Krizhevsky A., Sutskever I., Hinton G. E. (2012). ImageNet classification with deep convolutional neural networks. Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12); Lake Tahoe, Nevada, USA. pp. 1097–1105. https://doi.org/10.1145/3065386
Lawrence S., Giles C. L., Chung T. A., Back A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE Transactions on Neural Networks, Vol. 8, No. 1, pp. 98-113. https://doi.org/10.1109/72.554195
Lecun Y., Bengio Y., Hinton G. E. (2015). Deep learning. Nature, Vol. 521, No. 7553, pp. 436-444. https://doi.org/10.1038/nature14539
Lecun Y., Boser B., Denker J. S., Henderson D., Howard R., Hubbard W., Jackel L. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, Vol. 1, No. 4, pp. 541-551. https://doi.org/10.1162/neco.19220.127.116.111
Lecun Y., Bottou L., Bengio Y., Haffner P. (1998). Gradient-based learning applied to document recognition. In Proceedings of the IEEE, Vol. 86, No. 11, pp. 2278-2324. https://doi.org/10.1109/5.726791
Lecun Y., Bottou L., Bengio Y., Haffner P. (1999). Object recognition with gradient-based learning. Shape, Contour and Grouping in Computer Vision, pp. 823-823. https://doi.org/10.1007/3-540-46805-6_19
Moody J., Darken C. J. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, Vol. 1, No. 2, pp. 281-294. https://doi.org/10.1162/neco.1918.104.22.1681
Moon H., Phillips P. J. (2001). Computational and performance aspects of PCA-based face-recognition algorithms. Perception, Vol. 30, No. 3, pp. 303-321. https://doi.org/10.1068/p2896
Osuna E., Freund R., Girosit F. (1997). Training support vector machines: An application to face detection. In: Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.1997.609310
Petrovska-Delacrétaz D., Chollet G., Dorizzi B. (2009). Guide to biometric reference systems and performance evaluation. Springer, pp. 111. https://doi.org/10.1007/978-1-84800-292-0
Phillips J., Moon H., Rizvi S., Rauss P. (2000). The FERET evaluation methodology for face-recognition algorithms IEEE Trans. Pattern Anal. Mach. Intell., Vol. 22, pp. 1090-1104. https://doi.org/10.1109/34.879790
Samal A., Iyengar P. A. (1992). Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern Recognition, Vol. 25, No. 1, pp. 65-77. https://doi.org/10.1016/0031-3203(92)90007-6
Schmidhuber J. (2015). Deep learning in neural networks: An overview. Neural Networks, Vol. 61, pp. 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
Schroff F., Kalenichenko D., Philbin J. (2015). Facenet: A unified embedding for face recognition and clustering. In CVPR. https://doi.org/10.1109/CVPR.2015.7298682
Sun Y., Wang X., Tang X. (2014). Deeply learned face representations are sparse, selective, and robust. arXiv preprint arXiv: 1412.1265. https://doi.org/10.1109/CVPR.2015.7298907
Syafeeza A. R. (2014) Convolutional neural network for face recognition with pose and illumination variation. International Journal of Engineering and Technology (IJET). Vol. 6, No. 1.
Turk M., Pentland A. (1991). Eigenfaces for recognition. Journal of Cognitive Neurosicence, Vol. 3, No. 1, pp. 71-86. https://doi.org/10.1162/jocn.1922.214.171.124
Vincent P., Larochelle H., Lajoie I. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, Vol. 11, No. 12, pp. 3371-3408. https://doi.org/10.1016/j.mechatronics.2010.09.004
Vinod N., Hinton G. E. (2010). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel. https://doi.org/10.1.1.165.6419
Wright J., Yang A., Allen Y., Ganesh A., et al. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No 2, pp. 210-227. https://doi.org/US5640492 A
Yambor W. S., Draper B. A., Beveridge J. R. (2002). Analyzing PCA-based face recognition algorithms: Eigenvector selection and distance measures. In Empirical Evaluation Methods in Computer Vision, pp. 39-60. https://doi.org/10.1142/9789812777423_0003
Yosinski J., Clune J., Bengio Y., Lipson H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, pp. 3320-3328.
Zhang Y., Zhao D., Sun J. (2016). Adaptive convolutional neural network and it’s application in face recognition. Neural Processing Letters, Vol. 43, pp. 389-399. https://doi.org/10.1007/s11063-015-9420-y
Zhao W., Chellappa R., Phillips P. J., Rosenfeld A. (2003). Face recognition: A literature survey. ACM Computing Surveys (CSUR), Vol. 35, No. 4, pp. 399-458. https://doi.org/10.1145/954339.954342