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Towards a Better Training for Siamese CNNs on Kinship Verefication

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Modelling and Implementation of Complex Systems (MISC 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 64))

Abstract

Kinship verification from facial images in the wild is a recent problem that received an increasing interest from the computer vision research community. Due to the limited size of the existing datasets, applying Deep Learning approaches results in a model that overfits to the training data, therefore, the purpose of this study is to reduce the degree of overfitting when training a Deep Learning model on kinship datasets. To this end, we propose a new training mechanism for siamese convnets, in which we train the model on all images from all types of kinship relations instead of training on each of these subsets separately, then we evaluate the model on each subset individually. Experimental results demonstrated that using this training method resulted in better performance compared to training on each subset separately, and allowed to achieve results comparable to the most recent state of the art approaches. This paper focuses on the impact of adding more data over adding the gender information by separating kinship relation types in different subsets.

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References

  1. Fang, R., Tang, K.D., Snavely, N., Chen, T.: Towards computational models of kinship verification. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 1577–1580. IEEE (2010)

    Google Scholar 

  2. Zhou, X., Hu, J., Lu, J., Shang, Y., Guan, Y.: Kinship verification from facial images under uncontrolled conditions. In: Proceedings of the 19th ACM International Conference on Multimedia, pp. 953–956. ACM (2011)

    Google Scholar 

  3. Lu, J., Zhou, X., Tan, Y.P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 331–345 (2014)

    Article  Google Scholar 

  4. Hu, J., Lu, J., Tan, Y.P.: Sharable and individual multi-view metric learning. IEEE Trans. Pattern Anal. Mach. Intell. (2017)

    Google Scholar 

  5. Hu, J., Lu, J., Tan, Y.P., Yuan, J., Zhou, J.: Local large-margin multi-metric learning for face and kinship verification. IEEE Trans. Circuits Syst. Video Technol. (2017)

    Google Scholar 

  6. Xu, M., Shang, Y.: Kinship measurement on face images by structured similarity fusion. IEEE Access 4, 10280–10287 (2016)

    Article  Google Scholar 

  7. Yan, H., Lu, J., Deng, W., Zhou, X.: Discriminative multimetric learning for kinship verification. IEEE Trans. Inf. Forensics Secur. 9(7), 1169–1178 (2014)

    Article  Google Scholar 

  8. Zhou, X., Shang, Y., Yan, H., Guo, G.: Ensemble similarity learning for kinship verification from facial images in the wild. Inf. Fusion 32, 40–48 (2016)

    Article  Google Scholar 

  9. Yan, H., Lu, J., Zhou, X.: Prototype-based discriminative feature learning for kinship verification. IEEE Trans. Cybern. 45(11), 2535–2545 (2015)

    Article  Google Scholar 

  10. Bottino, A., Vieira, T.F., Ul Islam, I.: Geometric and textural cues for automatic kinship verification. Int. J. Pattern Recognit. Artif. Intell. 29(03), 1556001 (2015)

    Article  Google Scholar 

  11. Zhou, X., Yan, H., Shang, Y.: Kinship verification from facial images by scalable similarity fusion. Neurocomputing 197, 136–142 (2016)

    Article  Google Scholar 

  12. Qin, X., Tan, X., Chen, S.: Mixed bi-subject kinship verification via multi-view multi-task learning. Neurocomputing 214, 350–357 (2016)

    Article  Google Scholar 

  13. Lan, R., Zhou, Y.: Quaternion-michelson descriptor for color image classification. IEEE Trans. Image Process. 25(11), 5281–5292 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  14. Lan, R., Zhou, Y., Tang, Y.Y.: Quaternionic weber local descriptor of color images. IEEE Trans. Circuits Syst. Video Technol. 27(2), 261–274 (2017)

    Article  Google Scholar 

  15. Yan, H.: Kinship verification using neighborhood repulsed correlation metric learning. Image Vis. Comput. 60, 91–97 (2017)

    Article  Google Scholar 

  16. Patel, B., Maheshwari, R., Raman, B.: Evaluation of periocular features for kinship verification in the wild. Comput. Vis. Image Underst. 160, 24–35 (2017)

    Article  Google Scholar 

  17. Lu, J., Hu, J., Tan, Y.P.: Discriminative deep metric learning for face and kinship verification. IEEE Trans. Image Process. 26(9), 4269–4282 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  18. Zhang, K., Huang, Y., Song, C., Wu, H., Wang, L.: Kinship verification with deep convolutional neural networks. In: Proceedings of the British Machine Vision Conference (BMVC), pp. 148.1–148.12. BMVA Press (September 2015). https://doi.org/10.5244/C.29.148

  19. Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese” time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737–744 (1994)

    Google Scholar 

  20. Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)

    Google Scholar 

  21. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop. vol. 2 (2015)

    Google Scholar 

  22. Sellam, A., Azzoune, H.: All-subsets siamese convolutional neural network for kinship verification (2018). https://github.com/asellam/ASCNN

  23. Lu, J., Hu, J., Zhou, X., Shang, Y., Tan, Y.P., Wang, G.: Neighborhood repulsed metric learning for kinship verification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2594–2601. IEEE (2012)

    Google Scholar 

  24. Chen, X., An, L., Yang, S., Wu, W.: Kinship verification in multi-linear coherent spaces. Multimed. Tools Appl. 76(3), 4105–4122 (2017)

    Article  Google Scholar 

  25. Dehshibi, M.M., Shanbehzadeh, J.: Cubic norm and kernel-based bi-directional PCA: toward age-aware facial kinship verification. Vis. Comput., 1–18 (2017)

    Google Scholar 

  26. Faraki, M., Harandi, M.T., Porikli, F.: No fuss metric learning, a hilbert space scenario. Pattern Recognit. Lett. 98, 83–89 (2017)

    Article  Google Scholar 

  27. Mahpod, S., Keller, Y.: Kinship verification using multiview hybrid distance learning. Comput. Vis. Image Underst. 167, 28–36 (2018)

    Article  Google Scholar 

  28. Yang, Y., Wu, Q.: A novel kinship verification method based on deep transfer learning and feature nonlinear mapping. DEStech Transactions on Computer Science and Engineering (aiea) (2017)

    Google Scholar 

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Correspondence to Sellam Abdellah .

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Abdellah, S., Hamid, A. (2019). Towards a Better Training for Siamese CNNs on Kinship Verefication. In: Chikhi, S., Amine, A., Chaoui, A., Saidouni, D.E. (eds) Modelling and Implementation of Complex Systems. MISC 2018. Lecture Notes in Networks and Systems, vol 64. Springer, Cham. https://doi.org/10.1007/978-3-030-05481-6_18

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