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Practical age estimation using deep label distribution learning

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Abstract

Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age label makes age estimation algorithms inefficient. Deep label distribution learning (DLDL) which employs convolutional neural networks (CNN) and label distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough label distribution which covers all ages for any given age label. In this paper, a more practical label distribution paradigm is proposed: we limit age label distribution that only covers a reasonable number of neighboring ages. In addition, we explore different label distributions to improve the performance of the proposed learning model. We employ CNN and the improved label distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.

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References

  1. Geng X, Yin C, Zhou Z H. Facial age estimation by learning from label distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401–2412

    Article  Google Scholar 

  2. Geng X. Label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 2014, 28(7): 1734–1748

    Article  Google Scholar 

  3. Gao B B, Xing C, Xie C W, Wu J, Geng X. Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 2017, 26(6): 2825–2838

    Article  MathSciNet  Google Scholar 

  4. Geng X, Wang Q, Xia Y. Facial age estimation by adaptive label distribution learning. In: Proceeding of the 22nd International Conference on Pattern Recognition. 2014, 4465–4470

  5. Ling M G, Geng X. Soft video parsing by label distribution learning. Frontiers of Computer Science, 2019, 13(2): 302–317

    Article  Google Scholar 

  6. Li Y F, Liang D M. Safe semi-supervised learning: a brief introduction. Frontiers of Computer Science, 2019, 13(4): 669–676

    Article  Google Scholar 

  7. Liu X Y, Wang S T, Zhang M L. Transfer synthetic over-sampling for class-imbalance learning with limited minority class data. Frontiers of Computer Science, 2019, 13(5): 996–1009

    Article  Google Scholar 

  8. Zhao R, Niu X, Wu Y, Luk W, Liu Q. Optimizing CNN-based object detection algorithms on embedded FPGA platforms. In: Proceedings of the 13th International Symposium on Applied Reconfigurable Computing. 2017, 255–267

  9. He Z, Kan M, Zhang J, Chen X, Shan S. A fully end-to-end cascaded CNN for facial landmark detection. In: Proceeding of the 12th IEEE International Conference on Automatic Face and Gesture Recognition. 2017, 200–207

  10. Marmanis D, Wegner J D, Galliani S, Schindler K, Datu M, Stilla U. Semantic segmentation of aerial images with an ensemble of CNNs. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 3(3): 273–480

    Google Scholar 

  11. 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 Transactions on Pattern Analysis and Machine Intelligence, 2017, 41(1): 121–135

    Article  Google Scholar 

  12. Graves A, Jaitly N, Mohamed A R. Hybrid speech recognition with deep bidirectional LSTM. IEEE Automatic Speech Recognition and Understanding, 2014, 1(3): 273–278

    Google Scholar 

  13. Fan Y, Lu X J, Li D, Liu Y L. Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction. 2016, 445–450

  14. Chen Y, Yang X, Zhong B, Pan S, Chen D, Zhang H. CNN tracker: online discriminative object tracking via deep convolutional neural network. Applied Soft Computing, 2016, 2(38): 1088–1098

    Article  Google Scholar 

  15. Guo G, Mu G. Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition. 2011, 657–644

  16. Guo G, Mu G. Joint estimation of age, gender and ethnicity: CCA vs. PLS. In: Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. 2013, 1–6

  17. Horng W B. Classification of age groups based on facial features. Tamkang Journal of Science and Engineering, 2001, 4(3): 183–192

    Google Scholar 

  18. Othman Z A, Adnan D A. Age classification from facial images system. International Journal of Computer Science and Mobile Computing, 2014, 3(10): 291–303

    Google Scholar 

  19. Kwon Y H, Vitoria Lobo N D. Age classification from facial images. Computer Vision and Image Understanding, 1999, 74(1): 1–21

    Article  Google Scholar 

  20. Cootes T F, Edwards G J, Taylor C J. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 681–685

    Article  Google Scholar 

  21. Fu Y, Huang T S. Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia, 2008, 10(4): 578–584

    Article  Google Scholar 

  22. Geng X, Zhou Z H, Smithmiles K. Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(12): 2234–2240

    Article  Google Scholar 

  23. Hardoon D R, Szedmak S, Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Computation, 2004, 16(12): 2639–2664

    Article  Google Scholar 

  24. Geladi P, Kowalski B R. Partial least-squares regression: a tutorial. Analytica Chimica Acta, 1986, 185(1): 1–17

    Article  Google Scholar 

  25. Basak D, Srimanta P, Patranabis D C. Support vector regression. Neural Information Processing-letter and Reviews, 2007, 11(10): 203–224

    Google Scholar 

  26. Yi D, Lei Z, Li S Z. Age estimation by multi-scale convolutional network. In: Proceedings of the 12th Asian Conference on Computer Vision. 2014, 144–158

  27. Chen S, Zhang C, Dong M. Deep age estimation: from classification to ranking. IEEE Transactions on Multimedia, 2017, 20(8): 2209–2222

    Article  Google Scholar 

  28. Hu Z Z, Wen Y G. Facial age estimation with age difference. IEEE Transactions on Image Processing, 2017, 26(7): 3087–3097

    Article  MathSciNet  Google Scholar 

  29. Duan M, Li K, Lia K. An ensemble CNN2ELM for age estimation. IEEE Transactions on Information Forensics and Security, 2017, 99(1): 1–12

    Google Scholar 

  30. Geng X, Yin C, Zhou Z H. Facial age estimation by learning from label distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(10): 2401–2412

    Article  Google Scholar 

  31. Ricanek J K, Tesafaye T. Morph: a longitudinal image database of normal adult age-progression. In: Proceedings the 7th International Conference on Automatic Face and Gesture Recognition. 2006, 341–345

  32. Mathias M, Benenson R, Pedersoli M, Van Gool L. Face detection without bells and whistles. In: Proceedings of European Conference on Computer Vision. 2014, 720–735

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Acknowledgements

The authors thank the financial support of the China National Natural Science Foundation (61702095), Natural Science Foundation(njpj2018209) of Nanjing Tech University Pujiang Institute, Anhui Polytechnic University Scientific Research Foundation (S031702004), Natural Science Foundation of Fujian Province (2018J01806) and Scientific Research Program of Outstanding Talents in Universities of Fujian.

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Authors and Affiliations

  1. Pujiang Institute, Nanjing Tech University, Nanjing, 211200, China

    Huiying Zhang

  2. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Huiying Zhang, Yu Zhang & Xin Geng

Authors
  1. Huiying Zhang
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  2. Yu Zhang
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  3. Xin Geng
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Corresponding author

Correspondence to Xin Geng.

Additional information

Huiying Zhang is currently a lecturer in Pujiang Institute, Nanjing Tech University, China. She received her MS degree in college of computer science and technology from Nanjing University of Aeronautics and Astronautics, China in 2010. Her research interests include facial recognition, pattern recognition.

Yu Zhang is currently an associate professor with the School of Computer Science and Engineering, Southeast University, China. He received his BS and MS degrees in telecommunications engineering from Xidian University, China in 2001 and 2004, respectively, and PhD degree from Nanyang Technological University, Singapore in 2014. His research areas include computer vision, machine learning, object recognition, video analysis, human action analysis, 3D pose estimation.

Xin Geng received the BS and MS degrees in computer science from Nanjing University, China in 2001 and 2004, respectively, and the PhD degree from Deakin University, Australia in 2008. He joined the School of Computer Science and Engineering at Southeast University, China in 2008, and is currently a professor and vice dean of the school. He has authored over 50 refereed papers, and he holds five patents in these areas. His research interests include pattern recognition, machine learning, and computer vision.

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Zhang, H., Zhang, Y. & Geng, X. Practical age estimation using deep label distribution learning. Front. Comput. Sci. 15, 153318 (2021). https://doi.org/10.1007/s11704-020-8272-4

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  • DOI: https://doi.org/10.1007/s11704-020-8272-4

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