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http://dx.doi.org/10.22937/IJCSNS.2021.21.5.35

Deep Face Verification Based Convolutional Neural Network  

Fredj, Hana Ben (Universite de Monastir, Faculte des Sciences de Monastir, Laboratoire de Micro-electronique et Instrumentation)
Bouguezzi, Safa (Universite de Monastir, Faculte des Sciences de Monastir, Laboratoire de Micro-electronique et Instrumentation)
Souani, Chokri (Universite de Sousse, Institut Superieur des Sciences Appliquees et de Technologie de Sousse)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.5, 2021 , pp. 256-266 More about this Journal
Abstract
The Convolutional Neural Network (CNN) has recently made potential improvements in face verification applications. In fact, different models based on the CNN have attained commendable progress in the classification rate using a massive amount of data in an uncontrolled environment. However, the enormous computation costs and the considerable use of storage causes a noticeable problem during training. To address these challenges, we focus on relevant data trained within the CNN model by integrating a lifting method for a better tradeoff between the data size and the computational efficiency. Our approach is characterized by the advantage that it does not need any additional space to store the features. Indeed, it makes the model much faster during the training and classification steps. The experimental results on Labeled Faces in the Wild and YouTube Faces datasets confirm that the proposed CNN framework improves performance in terms of precision. Obviously, our model deliberately designs to achieve significant speedup and reduce computational complexity in deep CNNs without any accuracy loss. Compared to the existing architectures, the proposed model achieves competitive results in face recognition tasks
Keywords
Deep learning; Face recognition; Lifting scheme; CNN;
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1 M. Iqbal, M. S. I. Sameem, N. Naqvi, S. Kanwal, and Z.A. Ye, "deep learning approach for face recognition based on angularly discriminative features," Pattern Recognition Letters, vol. 128, pp. 414-419, 2019.   DOI
2 S. R Arashloo, andJ.Kittler, "Fast pose invariant face recognition using super coupled multiresolution Markov Random Fields on a GPU,"Pattern Recognition Letters, vol. 48,pp. 49-59, 2014.   DOI
3 K. He, andJ.Sun, "Convolutional neural networks at constrainedtime cost," IEEE Conference on Computer Vision and PatternRecognition (CVPR),pp.5353-60, 2015.
4 Y.Sun, X. Wang, and X.Tang, "Deep learning face representation by joint identification-verification," Advances in Neural Information Processing Systems, Montreal, Canada, pp.1988-1996, 2014.
5 A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet classification with deep convolutional neural networks," In Advances in neural information processing systems,pp. 1097-1105, 2012.
6 F. Schroff, D. Kalenichenko, and J. Philbin, "Facenet: A unified embedding for face recognition and clustering," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815-823, 2015.
7 H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, and W. Liu, "Cosface: Large margin cosine loss for deep face recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognitionpp. 265-5274, 2018.
8 Li, Z. M., Li, W. J., and Wang, J. "Self-Adapting Patch Strategies for Face Recognition," International Journal of Pattern Recognition and Artificial Intelligence, vol.34,pp. 2056002, 2020.   DOI
9 M. Kirby, and L. Sirovich, "Application of the Karhunen-Loeve procedure for the characterization of human faces," IEEE Transactions on Pattern analysis and Machine intelligence, vol.12,pp. 103-108,1990.   DOI
10 H. Wang, X. He, T. Hintz, and Q. Wu, "Fractal image compression on hexagonal structure," Journal of Algorithms & Computational Technology, vol.2, pp. 79-98, 2008.   DOI
11 Shi, Y., and Jain, A. K. "Probabilistic face embeddings," In Proceedings of the IEEE International Conference on Computer Vision, pp. 6902-6911, 2019.
12 Y. Wen, K.Zhang, Z. Li, and Y. Qiao, "A comprehensive study on center loss for deep face recognition," International Journal of Computer Vision, vol.127, pp. 668-683, 2019.   DOI
13 W. Ding, F. Wu, X. Wu, S. Li, and H. Li, "Adaptive directional lifting-based wavelet transform for image coding," IEEE Transactions on Image Processing, vol.16,pp. 416-427, 2017.   DOI
14 G. Hu, Y.Yang, D. Yi et al, "When face recognition meets with deep learning, an evaluation of convolutional neural networks for face recognition,"IEEE Int. Conf. on Computer Vision Workshops, Santiago, pp.142-1502015.
15 K.Simonyan, and A.Zisserman, "Very deep convolutional networks for large-scale image recognition,"arXiv preprint arXiv:1409.1556 , 2014.
16 Y. Gong, L. Liu, M. Yang, andL.Bourdev, "Compressing deep convolutional networks using vector quantization,"arXiv preprint arXiv:1412.6115, 2014.
17 W. Liu, Y. Wen, Z. Yu, M. Li, B.Raj, and L. Song, "Sphereface: Deep hypersphere embedding for face recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 212-220, 2017.
18 M. Masud, G. Muhammad, H. Alhumyani, S. Alshamrani, O. Cheikhrouhou, S. Ibrahim, and M. S. Hossain, "Deep learning- based intelligent face recognition in IoT-cloud environment," Computer Communications, vol. 152, pp. 215-222, 2020.   DOI
19 Q.Li, J.Yu, T.Kurihara, H.Zhang, andS. Zhan, "Deep convolutional neural network with optical flow for facial micro-expression recognition," Journal of Circuits, Systems and Computers, vol. 29, pp. 2050006, 2020.   DOI
20 Y. Sun,X. Wang, and X. Tang, "Deep learning face representation from predicting 10,000 classes," IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, pp.1891-1898, 2014.
21 S. Banerjee, and S. Das, "LR-GAN for degraded Face Recognition," Pattern Recognition Letters, vol. 116, pp. 246-253, 2018.   DOI
22 X. Wu, R. He, Z. Sun, and T.Tan, "A light cnn for deep face representation with noisy labels," IEEE Transactions on Information Forensics and Security, vol.13, pp. 2884-2896, 2018.   DOI
23 Wang, M., andDeng, W. "Deep face recognition with clustering based domain adaptation," Neurocomputing, 2020.
24 Lu, Z., Jiang, X., andKot, A. "Deep coupled resnet for low-resolution face recognition," IEEE Signal Processing Letters, vol.25, pp. 526-530, 2018.   DOI
25 K. Zhang, X. Ren, S. and Sun, J, "Deep residual learning for image recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 770-778, 2016.
26 A. Youssef, "Image downsampling and upsampling methods," National Institute of Standards and Technology, 1999.
27 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
28 J. Deng, J.Guo, N.Xue, and S. Zafeiriou, "Arcface: Additive angular margin loss for deep face recognition," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690-4699, 2019.
29 K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman, " Return of the devil in the details, Delving deep into convolutional nets," arXiv preprint arXiv:1405.3531, 2014.
30 C. Szegedy, W.Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, and A. Rabinovich, "Going deeper with convolutions," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
31 S. Munasinghe, C. Fookes, and S.Sridharan, "Human-level face verification with intra-personal factor analysis and deep face representation," IET Biometrics, vol. 7, pp. 467-473, 2018.   DOI
32 G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, andR. R. Salakhutdinov, "Improving neural networks by preventing co-adaptation of feature detectors,"arXiv preprint arXiv:1207.0580, 2012.
33 Liu, W., Wen, Y., Yu, Z., and Yang, M. "Large-margin softmax loss for convolutional neural networks," In ICML, pp. 507-516, 2016.
34 Y. Sun, Y. Chen, X. Wang, and X. Tang, "Deep learning face representation by joint identification-verification," In Advances in neural information processing systems, pp. 1988-1996, 2014.
35 D. Yi, Z. Lei, S. Liao, and S.Z. Li, "Learning face representation from scratch," arXiv preprint arXiv:1411.7923, 2014.
36 Y. Sun, X. Wang, and X. Tang, "Deeply learned face representations are sparse, selective, and robust," In: IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 2892-2900, 2015.
37 Hu, W., Huang, Y., Zhang, F., Li, R., Li, W., and Yuan, G. "SeqFace: make full use of sequence information for face recognition," arXiv preprint arXiv:1803.06524, 2018.
38 J. Xiang, and G. Zhu,. "Joint Face Detection and Facial Expression Recognition with MTCNN," In 2017 4th International Conference on Information Science and Control Engineering (ICISCE), pp. 424-427, 2017.