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Age and gender recognition with random occluded data augmentation on facial images

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Abstract

In this article, we propose the Random Occlusion, a data augmentation method on facial images using simple image processing techniques for age and gender recognition. Previous methods achieved promising results on constrained datasets with strict environmental settings, but the results on unconstrained datasets are still far from perfect. This article proposed a data augmentation method by altering the training images that resemble real-life photos to improve the performance of the networks by providing more varieties to the training samples. The proposed method adopted three simple occlusion techniques, Blackout, Random Brightness, and Blur, and each simulates a different kind of challenge that would be encountered in real-world applications. We verify the effectiveness of the proposed method by implementing the augmentation method on two convolution neural networks (CNNs), the modified AdienceNet and VGG16 to perform age and gender classification. The proposed augmentation method improves the age accuracy results of the modified AdienceNet and VGG16 by 1.0% and 0.8%, respectively; and gender accuracy results of the AdienceNet and VGG16 by 1.5% and 1.2%, respectively.

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References

  1. Abadi M, Agarwal A, Barham P, Brevdo E et al. (2015) TensorFlow: large-scale machine learning on heterogeneous systems.

  2. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence 28(12):2037–2041

    Article  Google Scholar 

  3. J. Chen, A. Kumar, R. Ranjan, V. M. Patel, A. Alavi and R. Chellappa (2016) A cascaded convolutional neural network for age estimation of unconstrained faces. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp 1–8.

  4. Chen Y, Xu W, Zuo J, Yang K (2019) The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier. Clust Comput 22:7665–7675

    Article  Google Scholar 

  5. Chen Y, Wang J, Liu S, Chen X, Xiong J, Xie J, Yang K (2019) Multiscale fast correlation filtering tracking algorithm based on a feature fusion model. Concurrency and Computation: Practice and Experience

  6. Y. Chen, J. Tao, L. Liu, J. Xiong, R. Xia, J. Xie, Q. Zhang and K. Yang (2020) Research of improving semantic image segmentation based on a feature fusion model. Journal of Ambient Intelligence and Humanized Computing. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-02066-z

  7. Chen Y, Tao J, Zhang Q, Yang K, Chen X, Xiong J, Xia R, Xie J (2020) Detection via the improved hierarchical principal component analysis method. Wireless communications and mobile computing 8822777

  8. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59

    Article  Google Scholar 

  9. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Transactions on Pattern Analysis & Machine Intelligence 23(6):681–685

    Article  Google Scholar 

  10. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  11. T. DeVries, and G.W. Taylor (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552.

  12. Dunn D, Higgins WE (1995) Optimal Gabor filters for texture segmentation. IEEE Trans Image Process 4(7):947–964

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. M.Y. El Dib, and M. El-Saban (2010) Human age estimation using enhanced bio-inspired features (EBIF). In: 2010 IEEE International Conference on Image Processing (ICIP), pp 1589–1592.

  15. ‘Flickr’, https://www.flickr.com/. Accessed 8 August 2019.

  16. Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32(11):1955–1976

    Article  Google Scholar 

  17. Y. Fu, T. Hospedales, T. Xiang, S. Gong and Y. Yao (2014) Interestingness prediction by robust learning to rank. In: European Conference on Computer Vision (ECCV), pp 488–503.

  18. A.C. Gallagher, and T. Chen (2009) Understanding images of groups of people. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 256–263.

  19. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio (2014) Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS), pp 2672–2680.

  20. Gunay A, Nabiyev VV (2008) Automatic age classification with LBP. In: 2008 23rd International Symposium on Computer and Information Sciences (ISCIS), pp 1–4.

  21. S. Hosseini, S.H. Lee, H.J. Kwon, H.I. Koo, and N.I. Cho (2018) Age and gender classification using wide convolutional neural network and Gabor filter. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp 1–3.

  22. M. Hu, Y. Zheng, F. Ren and H. Jiang (2014) Age estimation and gender classification of facial images based on Local Directional Pattern. In: 2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems (CCIS), pp 103–107.

  23. W. Hu, Y. Huang, L. Wei, F. Zhang, H. Li (2015) Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors

  24. T. Jabid, M.H. Kabir, and O. Chae (2010) Local directional pattern (LDP) for face recognition. In: 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE), pp. 329–330.

  25. Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Transactions on circuits and systems for video technology 14(1):4–20

    Article  Google Scholar 

  26. Y. Jia, E. Shellhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell (2014) Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international Conference on Multimedia (ACMMM), pp 675–678.

  27. S. Lapuschkin, A. Binder, K.R. Muller, and W. Samek (2017) Understanding and comparing deep neural networks for age and gender classification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 1629–1638.

  28. G. Levi, and T. Hassncer (2015) Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition workshops (CVPR), pp 34–42.

  29. Lu W, Zhang X, Lu H, Li F (2020) Deep hierarchical encoding model for sentence semantic matching. J Vis Commun Image Represent 102794.

  30. Luo Y, Qin J, Xiang X, Tan Y, Liu Q, Xiang L (2020) Coverless real-time image information hiding based on image block matching and dense convolutional network. J Real-Time Image Proc 17(1):125–135

    Article  Google Scholar 

  31. K. Luu, K. Ricanek, T.D. Bui, and C.Y. Suen (2009) Age estimation using active appearance models and support vector machine regression. In: 2009 IEEE 3rd international conference on biometrics: theory, applications, and systems (BTAS), pp 1-5.

  32. Maldonado R, Tansuhaj P, Muehling DD (2003) The impact of gender on ad processing: a social identity perspective. Acad Mark Sci Rev 3(3):1–15

    Google Scholar 

  33. V. Mnih, N. Heess, and A. Graves (2014) Recurrent models of visual attention. In: Proceedings of the 27th International Conference on Neural Information Processing System (NIPS), pp 2204–2212.

  34. V. Nair, and G.E. Hinton (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML), pp 807–814.

  35. ‘OpenCV. https://opencv.org/, Accessed 8 August 2019.

  36. Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12(1):145–151

    Article  Google Scholar 

  37. R. Ranjan, S. Sankaranarayanan, C.D. Castillo and R. Chellappa (2017) An all-in-one convolutional neural network for face analysis. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG), pp 17–24.

  38. K. Ricanek, T. Tesafaye (2006) Morph: A longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FG), pp 341–345.

  39. Rodríguez P, Curcurull G, Gonfaus JM, Roca FX, Gonzalez J (2017) Age and gender recognition in the wild with deep attention. Pattern Recogn 72:563–571

    Article  Google Scholar 

  40. R. Rothe, R. Timofte, and L. Van Gool (2015) Dex: Deep expectation of apparent age from a single image. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV), pp 252–257.

  41. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Li FF (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252

    Article  MathSciNet  Google Scholar 

  42. C. Shan (2010) Learning local features for age estimation on real-life faces. In: Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis, pp 23–28.

  43. Silberman N, Guadarrama S. ‘TensorFlow-Slim image classification model library. https://github.com/tensorflow/models/tree/master/research/slim. Accessed 8 August 2019

  44. K. Simonyan, and A. Zisserman (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

  45. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  46. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Auguelov, D. Erhan, V. Vanhoucke, A. Rabinovich (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, pp 1–9

  47. Tapia J, Perez C (2013) Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. IEEE Transactions on Information Forensics and Security 8(3):488–499

    Article  Google Scholar 

  48. T. van Laarhoven (2017) L2 regularization versus batch and weight normalization. arXiv preprint arXiv:1706.05350.

  49. Van Rossum G, Drake F (2009) Python 3 reference manual. Scotts Valley, CA

  50. J. Wang, and L. Perez (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.

  51. Wang W, Shen J (2018) Deep visual attention prediction. IEEE Trans Image Process 27(5):2368–2378

    Article  MathSciNet  Google Scholar 

  52. Wang W, Shen J, Ling H (2018) A deep network solution for attention and aesthetics aware photo cropping. IEEE Trans Pattern Anal Mach Intell 41(7):1531–1544

    Article  Google Scholar 

  53. Wang J, Qin J, Xiang X, Tan Y, Pan N (2019) CAPTCHA recognition based on deep convolutional neural network. Math Biosci Eng 16(5):5851–5861

    Article  MathSciNet  Google Scholar 

  54. L. Wolf, T. Hassner, and Y. Taigman (2008) Descriptor based methods in the wild. Faces in Real-Life Images workshop at the European Conference on Computer Vision (ECCV)

  55. J. Wolfshaar, M. F. Karaaba and M. A. Wiering (2015) Deep convolutional neural networks and support vector machines for gender recognition," in 2015 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 188–195.

  56. S. Wong, A. Gatt, V. Stamatescu and M. McDonnell (2016) Understanding data augmentation for classification: when to warp? In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp 1–6.

  57. Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters 23(10):1499–1503

    Article  Google Scholar 

  58. Zhang J, Xie Z, Sun J, Zou X, Wang J (2020) A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8:29742–29754

    Article  Google Scholar 

  59. B. Zoph, E.D. Cubuk, G. Ghiasi, T.Y. Lin, J. Shlens, and Q.V. Le (2019) Learning Data Augmentation Strategies for Object Detection. arXiv preprint arXiv:1906.11172,

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Acknowledgments

The authors would like to thank the Ministry of Science and Technology in Taiwan for supporting this research under MOST 106-2221-E-011-153-MY2.

Availability of data and material

The data that support the findings of this study are openly available in “Face Image Project” at https://talhassner.github.io/home/projects/Adience/Adience-data.html, reference number [23].

Funding

This study was funded by the Ministry of Science and Technology in Taiwan for supporting this research under MOST 106–2221-E-011-153-MY2.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, No. 43, Keelung Rd., Sec. 4, Da’an Dist., Taipei, Taiwan

    Chia-Yuan Hsu, Lu-En Lin & Chang Hong Lin

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  1. Chia-Yuan Hsu
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  2. Lu-En Lin
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Correspondence to Chang Hong Lin.

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All code for age and gender recognition with random occluded data augmentation on facial images associated with the current submission is available at https://github.com/cjwong96/AgeandGenderRecognition.

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Hsu, CY., Lin, LE. & Lin, C.H. Age and gender recognition with random occluded data augmentation on facial images. Multimed Tools Appl 80, 11631–11653 (2021). https://doi.org/10.1007/s11042-020-10141-y

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