Abstract
Face recognition in complex environments has attracted the attention of the research community in the last few years due to the huge difficulties that can be found in images captured in such environments. In this context, we propose to extract a robust facial description in order to improve facial recognition rate even in the presence of illumination, pose or facial expression problems. Our method uses texture descriptors, namely Mesh-LBP extracted from 3D Meshs. These extracted descriptors will then be used to train a Convolution Neural Networks (CNN) to classify facial images. Experiments on several datasets has shown that the proposed method gives promising results in terms of face recognition accuracy under pose, face expressions and illumination variation.
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References
Oloyede, M.O., Hancke, G.P., Myburgh, H.C.: A review on face recognition systems: recent approaches and challenges. Multimedia Tools Appl. 79(37), 27891–27922 (2020)
Anwarul, S., Dahiya, S.: A comprehensive review on face recognition methods and factors affecting facial recognition accuracy. In: Proceedings of International Conference on Robotics and Intelligent Control ICRIC, pp. 495–514 (2020)
Yin, Y., Jiang, S., Robinson, J.P., Fu, Y.: Dual-attention GAN for large-pose face frontalization. arXiv preprint arXiv:2002.07227 (2020)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res., 1755–1758 (2009)
Huber, P., et al.: A multiresolution 3D morphable face model and fitting framework. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 79–86 (2016)
Werghi, N., Tortorici, C., Berretti, S., Del Bimbo, A.: Boosting 3D LBP-based face recognition by fusing shape and texture descriptors on the mesh. IEEE Trans. Inf. Forensics Secur. 11(5), 964–979 (2016)
Wang, H., Hu, J., Deng, J.: Face feature extraction: a complete review. IEEE Access, 6001–6039 (2018)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)
Savran, A., et al.: Bosphorus database for 3D face analysis. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BioID 2008. LNCS, vol. 5372, pp. 47–56. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89991-4_6
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition (2008)
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: IEEE CVPR, pp. 529–534 (2011)
Zhu, X., Lei, Z., Yan, J., Yi, D., Li, S.Z.: High-fidelity pose and expression normalization for face recognition in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 787–796 (2015)
Hu, G., et al.: Face recognition using a unified 3D morphable model. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 73–89. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_5
Hu, G., et al.: Efficient 3D morphable face model fitting. Pattern Recognit. 67, 366–379 (2017)
Yu, Y., Songyao, J., Joseph, P.R., Yun, F.: Dual-attention GAN for large-pose face frontalization. arXiv preprint arXiv:2002.07227 (2020)
Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M.: Towards large-pose face frontalization in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3990–3999 (2017)
Hu, C., Feng, Z., Wu, X., Kittler, J.: Dual encoder-decoder based generative adversarial networks for disentangled facial representation learning. IEEE Access 8, 130159–130171 (2020)
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition (2015)
Deng, W., Chen, B., Fang, Y., Hu, J.: Deep correlation feature learning for face verification in the wild. IEEE Signal Process. Lett. 24(12), 1877–1881 (2017)
Sharma, S., Vijay, K.: Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning. Multimedia Tools Appl. (1–28) (2020)
Hariri, W., Tabia, H., Farah, N., Benouareth, A., Declercq, D.: 3D facial expression recognition using kernel methods on Riemannian manifold. Eng. Appl. Artif. Intell. 64, 25–32 (2017)
Deng, X., Da, F., Shao, H.: Efficient 3D face recognition using local covariance descriptor and Riemannian kernel sparse coding. Comput. Electr. Eng. 62, 81–91 (2017)
Lei, Y., Guo, Y., Hayat, M., Bennamoun, M., Zhou, X.: A two-phase weighted collaborative representation for 3D partial face recognition with single sample. Pattern Recognit. 52, 218–237 (2016)
Abbad, A., Abbad, K., Tairi, H.: 3D face recognition: multi-scale strategy based on geometric and local descriptors. Comput. Electr. Eng. 70, 525–537 (2018)
Zhang, Z., Da, F., Yu, Y.: Data-free point cloud network for 3D face recognition. arXiv, arXiv-1911 (2019)
Deng, X., Da, F., Shao, H., Jiang, Y.A.: Multi-scale three-dimensional face recognition approach with sparse representation-based classifier and fusion of local covariance descriptors. Comput. Electr. Eng. 85 (2020)
Yim, J., Jung, H., Yoo, B., Choi, C., Park, D., Kim, J.: Rotating your face using multi-task deep neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 676–684 (2015)
Deng, W., Hu, J., Wu, Z., Guo, J.: Lighting-aware face frontalization for unconstrained face recognition. Pattern Recognit. 68, 260–271 (2017)
Ding, C., Tao, D.: Pose-invariant face recognition with homography-based normalization. Pattern Recognit. 66, 144–152 (2017)
Koppen P, et al.: Gaussian mixture 3D morphable face model. Pattern Recognit. 74, 617–628 (2018)
Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8609–8613 (2013)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
Jingxin, B., Yinan, L., Shuo, Z.: 3D multi-poses face expression recognition based on action units. In: International Conference on Information Technology and Computer Communications (2019)
Rong, C., Xingming, Z., Yubei, L.: Feature-improving generative adversarial network for face frontalization. IEEE Access 8, 68842–68851 (2020)
Schroff, F., Dmitry, K., James, P.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Taigman, M.L.Y., Yang, M.: Deep learning face representation from predicting 10,000 classes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1891–1898 (2014)
Guo, G., Na, Z.: A survey on deep learning based face recognition. Comput. Vis. Image Underst. 189, 102805 (2019)
Gui, J., Sun, Z., Wen, Y., Tao, D., Ye, J.: A review on generative adversarial networks: algorithms, theory, and applications. arXiv preprint arXiv:2001.06937 (2020)
Ning, X., Nan, F., Xu, S., Yu, L., Zhang, L.: Multi-view frontal face image generation: a survey. Concur. Comput. Pract. Exp. (2020)
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Abed, R., Bahroun, S., Zagrouba, E. (2021). Toward a Robust Shape and Texture Face Descriptor for Efficient Face Recognition in the Wild. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_29
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