Adv-Kin: An Adversarial Convolutional Network for Kinship Verification

  • Qingyan Duan
  • Lei Zhang
  • Wei Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10568)


Kinship verification in the wild is an interesting and challenging problem, which aims to determine whether two unconstrained facial images are from the same family. Most previous methods for kinship verification can be divided as low-level hand-crafted features based shallow methods and kin data trained generic convolutional neural network (CNN) based deep methods. Nevertheless, these general methods cannot well mining the potential information implied in kin-relation data. Inspired by MMD and GAN, Adv-Kin method is proposed in this paper. The discrimination of deep features can be improved by introducing MMD loss (ML) to minimize the distribution difference between parents domain and children domain. In addition, we propose the adversarial loss (AL) that can further improve the robustness of CNN model. Extensive experiments on the benchmark KinFaceW-I, KinFaceW-II, Cornell KinFace and UB KinFace show promising results over many state-of-the-art methods.


Kinship verification Convolutional neural networks Maximum mean discrepancy Adversarial loss 



This work was supported by the National Science Fund of China under Grants (61771079, 61401048) and the Fundamental Research Funds for the Central Universities (No. 106112017CDJQJ168819).


  1. 1.
    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
  2. 2.
    Lu, J., Zhou, X., Tan, Y.P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. Proc. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 331–345 (2014)CrossRefGoogle Scholar
  3. 3.
    Li, L., Feng, X., Wu, X., Xia, Z., Hadid, A.: Kinship verification from faces via similarity metric based convolutional neural network. In: Campilho, A., Karray, F. (eds.) ICIAR 2016. LNCS, vol. 9730, pp. 539–548. Springer, Cham (2016). doi: 10.1007/978-3-319-41501-7_60 CrossRefGoogle Scholar
  4. 4.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning, pp. 513–520 (2011)Google Scholar
  6. 6.
    Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Computers & Operations Research, pp. 11–26 (2016)Google Scholar
  7. 7.
    Wang, M., Li, Z., Shu, X., Wang, J.: Deep kinship verification. In: Proceedings of the IEEE International Workshop on Multimedia Signal Processing, pp. 1–6 (2015)Google Scholar
  8. 8.
    Fang, R., Tang, K.D., Snavely, N., Chen, T.: Towards computational models of kinship verification. In: Proceedings of the 17th IEEE International Conference on Image Processing, pp. 1577–1580 (2010)Google Scholar
  9. 9.
    Yan, H., Lu, J., Zhou, X.: Prototype-based discriminative feature learning for kinship verification. IEEE Trans. Cybern. 45(11), 2535–2545 (2015)CrossRefGoogle Scholar
  10. 10.
    Shao, M., Xia, S., Fu, Y.: Genealogical face recognition based on UB KinFace database. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 60–65 (2011)Google Scholar
  11. 11.
    Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2534–2544 (2011)Google Scholar
  12. 12.
    Hu, J., Lu, J., Yuan, J., Tan, Y.-P.: Large margin multi-metric learning for face and kinship verification in the wild. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 252–267. Springer, Cham (2015). doi: 10.1007/978-3-319-16811-1_17 Google Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 2(10), 1499–1503 (2016)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Aaron, C., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)Google Scholar
  17. 17.
    Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: Proceedings of the International Conference on Machine Learning, pp. 97–105 (2015)Google Scholar
  18. 18.
    Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2539–2544 (2011)Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of Communication EngineeringChongqing UniversityChongqingChina
  2. 2.School of Computer and InformationHefei University of TechnologyHefeiChina

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