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Multi-Task Deep Metric Learning with Boundary Discriminative Information for Cross-Age Face Verification

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Abstract

Image based face verification has attracted extension attention in the fields of pattern recognition and intelligent vision. With difference in age, cross-age face verification from facial images remains a challenging work because of a large number of facial variations caused by shape, skin color and wrinkles and so on. This study proposes a multi-task deep metric learning with boundary discriminative information method called MDML-BDI. It learns a distance metric by exploring discriminative information among the interclass neighborhood samples, such that the distances between intraclass samples are as small as possible and that between interclass neighborhood samples are as far as possible. MDML-BDI learns hierarchical nonlinear transformations by integrating metric learning into the framework of multi-task deep neural network, such that a common shared layer shares the common transformation by multiple tasks, and the other independent layers learn individual task-special transformation for each task. Experimental results on FG-NET, CACD-VS and CALFW datasets show that MDML-BDI achieves satisfactory performance in terms of accuracy and receiver operating characteristic (ROC) curve.

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  3. Kavalionak, H., Gennaro, C., Amato, G., et al.: Distributed video surveillance using smart cameras. Journal of Grid Computing. 17(1), 59–77 (2019)

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  4. Ding, C.: Tao, D: Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence. 40(4), 1002–1014 (2018)

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  5. Ge, S., Zhao, S., Li, C., Li, J.: Low-resolution face recognition in the wild via selective knowledge distillation. IEEE Trans. Image Processing. 28(4), 2051–2062 (2018)

    MathSciNet  Google Scholar 

  6. Hu, J., Lu, J., Tan, Y. P.: Discriminative deep metric learning for face verification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1875-1882 (2014)

  7. Hu, J., Lu, J., Tan, Y. P.: Deep transfer metric learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 325-333 (2015)

  8. Belanova, E., Davis, J.P., Thompson, T.: Cognitive and neural markers of super-recognisers’ face processing superiority and enhanced cross-age effect. Cortex. 108(11), 92–111 (2018)

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  9. Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security. 9(12), 2170–2179 (2014)

    Google Scholar 

  10. Lanitis, A.: A survey of the effects of aging on biometric identity verification. International Journal of Biometrics. 2(1), 34 (2010)

    Google Scholar 

  11. Gong, D., Li, Z., Lin, D., Liu, J., Tang, X.: Hidden factor analysis for age invariant face recognition. In: Proceedings of the IEEE international conference on computer vision, pp. 2872-2879 (2013)

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  13. Shu, X., Tang, J., Lai, H., Liu, L., Yan, S.: Personalized age progression with aging dictionary. In: Proceedings of the IEEE international conference on computer vision, pp. 3970-3978 (2015)

  14. Pang, M., Cheung, Y.M., Wang, B., Liu, R.: Robust heterogeneous discriminative analysis for face recognition with single sample per person. Pattern Recogn. 89(5), 91–107 (2019)

    Google Scholar 

  15. Shakeel, M.S., Lam, K.M.: Deep-feature encoding-based discriminative model for age-invariant face recognition. Pattern Recogn. 93(9), 442–457 (2019)

    Google Scholar 

  16. Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: Face verification across age progression using discriminative methods. IEEE Trans. Information Forensics and security. 5(1), 82–91 (2010)

    Google Scholar 

  17. Chen, B.C., Chen, C.S., Hsu, W.H.: Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Trans. Multimedia. 17(6), 804–815 (2015)

    Google Scholar 

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  19. Chen, J. C., Ranjan, R., Kumar, A., Chen, C. H., Patel, V. M., Chellappa, R.: An end-to-end system for unconstrained face verification with deep convolutional neural networks. In: Proceedings of the IEEE international conference on computer vision workshops, pp. 118-126 (2015)

  20. Sun, Y., Wang, X., Tang, X.: Hybrid deep learning for face verification. In: Proceedings of the IEEE international conference on computer vision, pp. 1489-1496 (2013)

  21. Soleimani, A., Araabi, B.N., Fouladi, K.: Deep multitask metric learning for offline signature verification. Pattern Recogn. Lett. 80(9), 84–90 (2016)

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  22. Cai, X., Wang, C., Xiao, B., Chen, X., Zhou, J.: Deep nonlinear metric learning with independent subspace analysis for face verification. In: Proceedings of the 20th ACM international conference on Multimedia, pp. 749-752 (2012)

  23. Lu, J., Zhou, X., Tan, Y.P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Pattern Analysis and Machine Intelligence. 36(2), 331–345 (2014)

    Google Scholar 

  24. Zhang, Y., Cheng, X., Chen, L., Shen, H.: Energy-efficient tasks scheduling heuristics with multi-constraints in virtualized clouds. Journal of Grid Computing. 16(3), 459–475 (2018)

    Google Scholar 

  25. Deshpande, P.D., Mukherji, P., Tavildar, A.S.: Accuracy enhancement of biometric recognition using iterative weights optimization algorithm. EURASIP J. Inf. Secur. 6(1), (2019)

  26. Wang, X., Zheng, W.S., Li, X., Zhang, J.: Cross-scenario transfer person reidentification. IEEE Trans. Circuits and Systems for Video Technology. 26(8), 1447–1460 (2016)

    Google Scholar 

  27. Wang, X., Zhang, C., Zhang, Z. Boosted multi-task learning for face verification with applications to web image and video search. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 142-149 (2009)

  28. Zhang, Z., Luo, P., Loy, C. C., Tang, X.: Facial landmark detection by deep multi-task learning. In: European conference on computer vision, pp. 94-108 (2014)

  29. Li, Y., Tian, X., Liu, T., Tao, D.: On better exploring and exploiting task relationships in multi-task learning: joint model and feature learning. IEEE Trans. Neural Networks and Learning Systems. 29(5), 1975–1985 (2018)

    MathSciNet  Google Scholar 

  30. Yang, P., Huang, K., Hussain, A.: A review on multi-task metric learning. Big Data Analytics. 3(1), 1–23 (2018)

    Google Scholar 

  31. Zheng, Y., Fan, J., Zhang, J., Gao, X.: Hierarchical learning of multi-task sparse metrics for large-scale image classification. Pattern Recogn. 67(7), 97–109 (2017)

    Google Scholar 

  32. Ma, L., Yang, X., Tao, D.: Person re-identification over camera networks using multi-task distance metric learning. IEEE Trans. Image Processing. 23(8), 3656–3670 (2014)

    MATH  MathSciNet  Google Scholar 

  33. Zhang, Y., Yeung, D.Y.: Transfer metric learning with semi-supervised extension. ACM Trans. Intelligent Systems and Technology (TIST). 3(3), 54:1-28 (2012)

    Google Scholar 

  34. Face and Gesture Recognition Working group. Fg-net aging database. http://www.fgnet.rsunit.com/

  35. Chen, B.C., Chen, C.S., Hsu, W.: Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Trans. Multimedia. 17(6), 804–815 (2015)

    Google Scholar 

  36. T. Zheng and W. Deng, Cross-pose LFW: A database for studying cross-pose face recognition in unconstrained environments, Beijing University of Posts and Telecommunications, Technical Report 18-01, 2018

  37. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Google Scholar 

  38. J. Goldberger, G. Hinton, S. Roweis, R. Salakhutdinov: Neighbourhood Components Analysis. In: NIPS'04 Proceedings of the 17th International Conference on Neural Information Processing Systems, pp. 513-520 (2005)

  39. Davis, J. V., Kulis, B., Jain, P., Sra, S., Dhillon, I. S.: Information-theoretic metric learning. In: Proceedings of the 24th international conference on Machine learning, pp. 209-216 (2007)

  40. Koestinger, M., Hirzer, M., Wohlhart, P., Roth, P. M., Bischof, H.: Large scale metric learning from equivalence constraints. In: 2012 IEEE conference on computer vision and pattern recognition, pp. 2288-2295 (2012)

  41. Tao, D., Jin, L., Wang, Y., Yuan, Y., Li, X.: Person re-identification by regularized smoothing kiss metric learning. IEEE Trans. Circuits and Systems for Video Technology. 23(10), 1675–1685 (2013)

    Google Scholar 

  42. Liong, V. E., Lu, J., Ge, Y.: Regularized Bayesian metric learning for person re-identification. In: European Conference on Computer Vision, pp. 209-224 (2014)

  43. Huang, G. B.: Learning hierarchical representations for face verification with convolutional deep belief Networks. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2518-2525 (2012)

  44. Cai, X., Wang, C., Xiao, B., Chen, X., Zhou, J.: Deep nonlinear metric learning with independent subspace analysis for face verification. In: ACM Press the 20th ACM international conference, pp. 749-752 (2012)

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61702225 and 61806026, by the Natural Science Foundation of Jiangsu Province under Grant BK20160187 and BK 20180956.

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Authors and Affiliations

  1. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, China

    Tongguang Ni, Xiaoqing Gu & Cong Zhang

  2. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Weibo Wang

  3. Viterbi School of Engineering, University of Southern California, Los Angeles, CA, 90089, USA

    Yiqing Fan

  4. College of International Education, Sichuan International Studies University, Chongqin, 400031, China

    Yiqing Fan

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  1. Tongguang Ni
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  2. Xiaoqing Gu
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Correspondence to Xiaoqing Gu.

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Ni, T., Gu, X., Zhang, C. et al. Multi-Task Deep Metric Learning with Boundary Discriminative Information for Cross-Age Face Verification. J Grid Computing 18, 197–210 (2020). https://doi.org/10.1007/s10723-019-09495-x

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