Abstract
With the progress and development of mobile camera and video surveillance, it becomes more efficiently to collect multiple face images for each query. Face recognition based on image set has attracted more and more attention in the community of computer vision and the application of biometrics. In this paper, instead of using handcraft features, we proposed to utilize the deep feature (e.g., convolutional neural network feature) in the application of image-set face recognition. In order to fully explore the discrimination of original query samples and the query virtual nearest point, we proposed a novel joint collaborative representation with a newly designed class-level similarity constraint on the coding coefficients. An alternative solving algorithm is proposed to solve the proposed model. Two experiments were conducted on the YouTube Face database and a new image-set database established based on Labeled Faced in the Wild (LFW). The result of experiments show that our approach has more advantages than previous image-set face recognition approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Huang, Z.H., Li, W.J., Shang, J., Wang, J., Zhang, T.: Non-uniform patch based face recognition via 2D-DWT. Image Vis. Comput. 37(5), 12–19 (2015)
Liu, H.D., Yang, M., Gao, Y., Cui, C.Y.: Local histogram specification for face recognition under varying lighting condictions. Image Vis. Comput. 32(5), 335–347 (2014)
Moeini, A., Moeini, H., Faez, K.: Unrestricted pose-invariant face recognition by sparse dictionary matrix. Image Vis. Comput. 36(4), 9–22 (2015)
Yang, M., Liu, W., Shen, L.: Joint regularized nearest points for image set based face recognition. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–7. IEEE (2015)
Hu, Y.Q., Mian, A.S., Owens, R.: Face recognition using sparse approximated nearest points between image sets. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1992–2004 (2012)
Yang, M., Zhu, P.F., Van Gool, L., Zhang, L.: Face recognition based on regularized nearest points between image sets. In: Proceedings of FG (2013)
Wu, Y., Minoh, M., Mukunoki, M.: Collaboratively regularized nearest points, In: Proceedings of BMVC, (2013)
Herrmann, C., Willersinn, D., Beyerer, J.: Low-Resolution Convolutional Neural Networks for Video Face Recognition. In: AVSS (2016)
Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-base face recognition using probabilistic appearance manifolds. In: Proceedings of CVPR (2003)
Arandjelovic, O., Shakhnarovich, G., Fisher, J., Cipolla, R., Darrel, T.: Face recognition with image sets using manifold density divergence. In: Proceedings of CVPR (2005)
Shakhnarovich, G., Fisher, J.W., Darrell, T.: Face recognition from long-term observations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 851–865. Springer, Heidelberg (2002). doi:10.1007/3-540-47977-5_56
Yamaguchi, O., Fukui, K., Maeda, K.-i.: Face recognition using temporal image sequence. In: Proceedings of FG (1998)
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). doi:10.1007/978-3-319-46478-7_31
Kim, T.K., Arandjelovic, O., Cipolla, R.: Discriminative learning and recognition of image set classes using canonical correlations. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1005–1018 (2007)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 21–227 (2009)
Shah, S.A.A., Bennamoun, M., Boussaid, F.: Iterative deep learning for image set based face and object recognition. Neurocomputing 174, 866–874 (2016)
Zhang, L., Yang, M., Feng, X.C.: Sparse representation or collaborative representation: which helps face recognition? In: Proceedings of ICCV (2011)
Cevikalp, H., Triggs, B.: Face recognition based on image sets. In: Proceedings of CVPR (2010)
Fumin, S., Chunhua, S., Zhou, X., Yang, Y., Shen, H.T.: Face image classification by pooling raw features. Pattern Recogn. 54(6), 94–103 (2016)
Shen, F., Shen, C., van den Hengel, A., Tang, Z.: Approximate least trimmed sum of squares fitting and applications in image analysis. IEEE Trans. Image Process. 22(5), 1836–1847 (2013)
Shen, F., Yang, Y., Zhou, X., Liu, X., Shao, J.: Face identification with second-order pooling in single-layer networks. Neurocomputing 187, 11–18 (2016)
Hayat, M., Bennamoun, M., An, S.: Deep reconstruction models for image set classification. IEEE Trans. Pattern Anal. Mach. Intell. 37(4), 713–727 (2015)
Acknowledgment
This work is partially supported by the National Natural Science Foundation for Young Scientists of China (no. 61402289), and National Science Foundation of Guang-dong Province (no. 2014A030313558).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, H., Yang, M. (2017). Joint Collaborative Representation with Deep Feature for Image-Set Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-69923-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69922-6
Online ISBN: 978-3-319-69923-3
eBook Packages: Computer ScienceComputer Science (R0)