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, Volume 103, Issue 2, pp 1195–1206 | Cite as

Improving Deep Learning Feature with Facial Texture Feature for Face Recognition

  • Yunfei LiEmail author
  • Zhaoyang Lu
  • Jing Li
  • Yanzi Deng


Face recognition in the reality, is a challenging problem, due to varieties in illumination, background, pose etc. Recently, the deep learning based face recognition algorithm is able to learn effective face features to obtain a very impressive performance. However, this kind of face recognition algorithm completely relies on the machine learning based face features, while ignores the useful experience in hand-craft features which have been studied in a long period. Therefore, a face recognition based on facial texture feature aided deep learning feature (FTFA-DLF) is proposed in this paper. The proposed FTFA-DLF is able to combine the benefits of deep learning and hand-craft features. In the proposed FTFA-DLF method, the hand-craft features are texture features extracted from the eyes, nose, and mouth regions. Then, the hand-craft features are used to aid deep learning features by adding both deep learning and hand-craft features into the objective function layer, which adaptively adjusts the deep learning features so that it can better cooperate with the hand-craft features and obtain a better face recognition performance. Experimental results show that the proposed face recognition algorithm on the LFW face database to achieve the accuracy rate of 97.02%.


Face recognition Convolution neural network Facial texture feature Feature fusion 



This work was supported in part by the National Natural Science Foundation of China under the Grants 61502364, in part by the Fundamental Research Funds for the Central Universities under the Grant K50510010007, and in part by Weinan normal university Funds 16YKS001.


  1. 1.
    Dix, A. (2009). Human-computer interaction. In Encyclopedia of database systems (pp. 1327–1331). New York: Springer.Google Scholar
  2. 2.
    Arigbabu, O. A., et al. (2015). Integration of multiple soft biometrics for human identification. Pattern Recognition Letters, 68, 278–287.CrossRefGoogle Scholar
  3. 3.
    Husain, I., et al. (2016). Attendance system based on face recognition by using raspberry Pi. International Journal of Research, 3(3), 22–26.Google Scholar
  4. 4.
    Feng, D., Siu, W.-C., & Zhang, H. J. (2013). Multimedia information retrieval and management: Technological fundamentals and applications. Dordrecht: Springer.zbMATHGoogle Scholar
  5. 5.
    Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.CrossRefGoogle Scholar
  6. 6.
    Zhang, L., et al. (2007). Face detection based on multi-block LBP representation. In Advances in biometrics (pp. 11–18). London: Springer.Google Scholar
  7. 7.
    Albiol, A., et al. (2008). Face recognition using HOGCEBGM. Pattern Recognition Letters, 29(10), 1537–1543.CrossRefGoogle Scholar
  8. 8.
    Dniz, O., et al. (2011). Face recognition using histograms of oriented gradients. Pattern Recognition Letters, 32(12), 1598–1603.CrossRefGoogle Scholar
  9. 9.
    Lei, Z., et al. (2008). Gabor volume based local binary pattern for face representation and recognition. In 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG’08, 2008.Google Scholar
  10. 10.
    Luo, J., et al. (2007). Person-specific SIFT features for face recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2007.Google Scholar
  11. 11.
    Liao, S., Jain, A. K., & Li, S. Z. (2013). Partial face recognition: Alignment-free approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(5), 1193–1205.CrossRefGoogle Scholar
  12. 12.
    Huang, G. B., Lee, H., Learned-Miller, E. (2012). Learning hierarchical representations for face verification with convolutional deep belief networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.Google Scholar
  13. 13.
    Sun, Y., Wang, X., Tang, X. (2013). Hybrid deep learning for face verification. In Proceedings of the IEEE International Conference on Computer Vision.Google Scholar
  14. 14.
    Deepface, Wolf, L. (2014). Closing the gap to human-level performance in face verification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.Google Scholar
  15. 15.
    Sun, Y., Wang, X., Tang, X. (2014). Deep learning face representation from predicting 10,000 classes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google Scholar
  16. 16.
    Sun, Y., et al. (2014). Deep learning face representation by joint identification-verification. In Advances in Neural Information Processing Systems.Google Scholar
  17. 17.
    Silapachote, P., Karuppiah, D. R., & Hanson, A. R. (2005). Feature selection using AdaBoost for face expression recognition. Massachusetts Univ Amherst Dept Of Computer Science.Google Scholar
  18. 18.
    Qin, J., He, Z.-S. (2005). A SVM face recognition method based on Gabor-featured key points. In Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2005. IEEE.Google Scholar
  19. 19.
    Sharma, A., Jacobs, D.W. (2011). Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. IEEE.Google Scholar
  20. 20.
    Chen, D., Cao, X., Wang, L., Wen, F., & Sun, J. (2012). Bayesian face revisited: A joint formulation. In European Conference on Computer Vision, 2012, Florence, Italy (pp. 566–579). Springer.Google Scholar
  21. 21.
    Davis, J. V., et al. (2007). Information-theoretic metric learning. In Proceedings of the 24th international conference on Machine learning ACM.Google Scholar
  22. 22.
    Huang, G. B. et al. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments., Technical Report 07–49. Amherst: University of Massachusetts.Google Scholar
  23. 23.
    Wolf, L., Hassner, T., Maoz, I. (2011). Face recognition in unconstrained videos with matched background similarity. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. IEEE.Google Scholar
  24. 24.
    Zhu, X., Ramanan, D. (2012). Face detection, pose estimation, and landmark localization in the wild. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. IEEE.Google Scholar
  25. 25.
    Yi, D., et al. (2014). Learning face representation from scratch. arXiv preprint arXiv:1411.7923. Accessed 28 Nov 2014.
  26. 26.
    Ioffe, S., Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. Accessed 2 Mar 2015.
  27. 27.
    Chen, D., Xudong, C., Fang, W., & Jian, S. (2013). Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification. In IEEE Conference on Computer vision and pattern recognition (CVPR), 2013 (pp. 3025–3032). IEEE.Google Scholar
  28. 28.
    LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 22782324.CrossRefGoogle Scholar
  29. 29.
    A. Vedaldi, Lenc, K. (2015). Matconvnet: Convolutional neural networks for matlab, In Proceedings of the 23rd ACM international conference on Multimedia, ACM, pp. 689C692.Google Scholar
  30. 30.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T. Caffe: Convolutional architecture for fast feature embedding, arXiv preprint arXiv:1408.5093. Accessed 20 Jun 2014.
  31. 31.
    Chen, D., Cao, X., Wen, F., Sun, J. (2013). Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 30253032.Google Scholar
  32. 32.
    K. He, Zhang, X., Ren, S., et al. Deep Residual Learning for Image Recognition, arXiv preprint arXiv:1512.03385. Accessed 10 Dec 2015.
  33. 33.
    Wen, Y., Zhang, K., Li, Z., Yu, Q. (2016). A discriminative feature learning approach for deep face recognition, In European Conference on Computer Vision, pp. 499–515.Google Scholar
  34. 34.
    Ding, C., Choi, J., Tan, D., & Davis, L. (2016). Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence, 38(3), 518.CrossRefGoogle Scholar
  35. 35.
    Ding, H., Zhou, S., Chellappa, R. (2017). FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition, In IEEE International Conference on Automatic Face & Gesture Recognition IEEE, pp. 118–126.Google Scholar
  36. 36.
    Wu, Y., Liu, H., Li, J., & Fu, Y. (2017). Deep Face Recognition with Center Invariant Loss. In ACM Multimedia-The matic Workshops. CA, USA ACM: Mountain View.Google Scholar
  37. 37.
    Waisy, A., Qahwaji, R., Ipson, S., Fahdawi, S. (2017). A multimodal deep learning framework using local feature representations for face recognition, In Machine Vision & Applications pp. 1–20.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Telecommunications EngineeringXidian UniversityXianChina
  2. 2.School of Network Security and InformationWeinan Normal UniversityWeinanChina

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