Transfer Learning for Facial Attributes Prediction and Clustering

  • Luca Anzalone
  • Paola Barra
  • Silvio BarraEmail author
  • Fabio Narducci
  • Michele Nappi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1122)


Notwithstanding the enhancement obtained in the last decade researches, the recognition of facial attributes is still today a trend. Besides the mere face recognition, the singular face features, like mouth, nose and hair, are considered as soft biometrics; these can be useful for human identification in cases the face is partially occluded, and only some regions are visible. In this paper we propose a model generated by transfer learning approach for the recognition of the face attributes. Also, an unsupervised clustering model is described, which is in charge of dividing and grouping faces based on their characteristics. Furthermore, we show how clusters can be evaluated by a compact summary of them, and how Deep Learning models should be properly trained for attribute prediction tasks.


Attribute clustering k-means Face attributes Transfer learning Cluster summary 



A special thank goes to the students Luca Anzalone, Marialuisa Trere and Simone Faiella for having conducted the experiments and proposed the model.


  1. 1.
    Abate, A.F., Barra, P., Bisogni, C., Nappi, M., Ricciardi, S.: Near real-time three axis head pose estimation without training. IEEE Access 7, 64256–64265 (2019). Scholar
  2. 2.
    Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding (2006)Google Scholar
  3. 3.
    Barra, P., Bisogni, C., Nappi, M., Ricciardi, S.: Fast quadtree-based pose estimation for security applications using face biometrics. In: Au, M.H., et al. (eds.) NSS 2018. LNCS, vol. 11058, pp. 160–173. Springer, Cham (2018). Scholar
  4. 4.
    Barra, S., De Marsico, M., Galdi, C., Riccio, D., Wechsler, H.: Fame: Face authentication for mobile encounter. In: 2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, pp. 1–7, September 2013.
  5. 5.
    Barra, S., Castiglione, A., Narducci, F., Marsico, M.D., Nappi, M.: Biometric data on the edge for secure, smart and user tailored access to cloud services. Future Gener. Comput. Syst. (2019). Scholar
  6. 6.
    Fenu, G., Marras, M.: Leveraging continuous multi-modal authentication for access control in mobile cloud environments. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 331–342. Springer, Cham (2017). Scholar
  7. 7.
    Fenu, G., Marras, M.: Controlling user access to cloud-connected mobile applications by means of biometrics. IEEE Cloud Comput. 5(4), 47–57 (2018). Scholar
  8. 8.
    Fenu, G., Marras, M., Meles, M.: A learning analytics tool for usability assessment in moodle environments. J. E-Learn. Knowl. Soc. 13(3), 23–34 (2017). Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015)Google Scholar
  10. 10.
    Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database forstudying face recognition in unconstrained environments (2008)Google Scholar
  11. 11.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. University of Toronto (2012)Google Scholar
  12. 12.
  13. 13.
    Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: Deep hypersphere embedding for face recognition. Carnegie Mellon University and Sun Yat-Sen University, Georgia Institute of Technology (2017)Google Scholar
  14. 14.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Large-scale celebfaces attributes (celebA) dataset. The Chinese University of Hong Kong, Multimedia Laboratory (2015)Google Scholar
  15. 15.
    Otto, C., Wang, D., Jain, K.: Clustering millions of faces by identity Google Scholar
  16. 16.
    Raina, R., Battle, A., Lee, H., Packer, B., Ng, A.Y.: Self-taught learning: transfer learning from unlabeled data. In: Proceedings of the 24th International Conference on Machine Learning, pp. 759–766. ACM (2007)Google Scholar
  17. 17.
    Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE (2017)Google Scholar
  18. 18.
    Rosebrock, A.: Face clustering with Python (2018)Google Scholar
  19. 19.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge (2015)Google Scholar
  20. 20.
    Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)Google Scholar
  21. 21.
    Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clusteringGoogle Scholar
  22. 22.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015)Google Scholar
  23. 23.
    Szegedy, C., et al.: Going deeper with convolutions (2014)Google Scholar
  24. 24.
    Wold, S., Esbensen, K., Geladi, P.: Principal component analysis (1987)CrossRefGoogle Scholar
  25. 25.
    Yang, S., Luo, P., Loy, C.C., Tang, X.: From facial parts responses to face detection: a deep learning approach. In: The IEEE International Conference on Computer Vision (ICCV), December 2015Google Scholar
  26. 26.
    Zeiler, M.D.: ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
  27. 27.
    Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016). Scholar
  28. 28.
    Zhu, X., Ramann, D.: Face detection, pose estimation, and landmark localization in the wildGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly
  2. 2.Department of Computer ScienceUniversity of SalernoSalernoItaly
  3. 3.Department of Science and TechnologyUniversity of Naples “Parthenope”NaplesItaly

Personalised recommendations