Advertisement

Face Recognition Using Transfer Learning on UFI Dataset

  • Soumik Ranjan DasguptaEmail author
  • Srinibas Rana
Conference paper
  • 74 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 12)

Abstract

Face Recognition is one of the most popular ways of biometric verification that are being applied worldwide, because of the convenience it offers. This encompasses large scale applications like corporate attendance systems to smaller ones such as unlocking hand-held devices and other various such kinds of applications. With the evolution of deep learning, face recognition systems have become increasingly accurate. One of the major reasons deep learning has become so popular for these types of tasks is because it does not require hand-crafted features. However, a major disadvantage of creating a deep learning model to recognize faces from scratch is the primary requirement of a huge amount of data. To counter this particular problem, another extremely popular technique called transfer learning is used to make the training process faster and with less data. In the current work, a novel real-world benchmark dataset is taken and the benchmark accuracy on it is increased by a large margin. The model in the current approach uses the concept of Siamese networks where triplets are generated for training. The triplets consist of an anchor image, a positive image of the same person and a negative image of a different person. This approach is particularly useful in this case because the amount of available data is less, along with the problem of class imbalance. The performance of the various models are compared with the previous results obtained using various features and classifiers and against one another.

Keywords

Face recognition Transfer learning Deep learning Computer vision 

References

  1. 1.
    Lenc, L., Král, P.: Unconstrained facial images: database for face recognition under real-world conditions. In: 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, 25–31 October 2015, Cuernavaca, Mexico. Springer (2015)Google Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 12, 2037–2041 (2006)CrossRefGoogle Scholar
  3. 3.
    Král, P., Lenc, L., Vrba, A.: Enhanced local binary patterns for automatic face recognition. arXiv preprint arXiv:1702.03349 (2017)
  4. 4.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)CrossRefGoogle Scholar
  6. 6.
    Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., Zhang, G.: Transfer learning using computational intelligence: a survey. Knowl.-Based Syst. 80, 14–23 (2015)CrossRefGoogle Scholar
  7. 7.
    Cao, X., Wipf, D., Wen, F., Duan, G., Sun, J.: A practical transfer learning algorithm for face verification. In: Proceedings of the IEEE international conference on computer vision, pp. 3208–3215 (2013)Google Scholar
  8. 8.
    Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)Google Scholar
  9. 9.
    Geng, M., Wang, Y., Xiang, T., Tian, Y.: Deep transfer learning for person re-identification. arXiv preprint arXiv:1611.05244 (2016)
  10. 10.
    Schroff, F., Kalenichenko, D., Philbin, J.: 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)Google Scholar
  11. 11.
    Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, no. 3, p. 6 (2015)Google Scholar
  12. 12.
    Cao, Q., Shen, L., Xie, W., Parkhi, O. M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2018, pp. 67–74. IEEE (2018)Google Scholar
  13. 13.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  14. 14.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  15. 15.
    Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Jalpaiguri Government Engineering CollegeJalpaiguriIndia

Personalised recommendations