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CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation

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Abstract

Automatic age estimation from the face images is a growing research interest nowadays. Various literature works have contributed towards the age detection scheme, besides only a few have resulted in providing good performance. This is due to the influence of the external factors, such as environment, lifestyle, and various expressions present in the face image. This paper proposes a deep belief network with the crow optimization algorithm for the age detection purpose. The proposed Crow Deep Belief Network (CDBN) finds the age of the person in the image through the initial training with the face features. The features for the training of the proposed CDBN are provided by the scattering transform and the Active Appearance Model (AAM). The training of the CDBN with the features provides the optimal weights used for the age detection. The experimentation of the proposed CDBN is done by four standard databases, namely the IMDB database, the Adience database, the AFAD database, and the FG-NET database based on the metrics, such as Mean Absolute Error (MAE), Accuracy of error of one age category (AEO) and Accuracy of an Exact Match (AEM). Among them, the proposed model has the minimum MAE with a value of 2.186 for FG-NET database, and maximum AEO and AEM with the values of 0.972, and 0.971, respectively for IMDB database.

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References

  1. Xing, J., Li, K., Hu, W., Yuan, C., & Ling, H. (June 2017). Diagnosing deep learning models for high accuracy age estimation from a single image. Pattern Recognition, 66, 106–116.

    Article  Google Scholar 

  2. Wang, S., Tao, D., & Yang, J. (2016). Relative attribute SVM+ learning for age estimation. IEEE Transactions on Cybernetics, 46(3), 827–839.

    Article  Google Scholar 

  3. Hu, C., Gong, L., Wang, T., & Feng, Q. (June 2015). Effective human age estimation using a two-stage approach based on lie Algebrized Gaussians feature. Multimedia Tools and Applications, 74(11), 4139–4159.

    Article  Google Scholar 

  4. Zhao, W., & Wang, H. (2015). Facial age estimation based on advanced ordinal ranking. Electronics Letters, 51(12), 903–904.

    Article  Google Scholar 

  5. Guo, G. G., Mu, G. W., Fu, Y., & Huang, T. S. (2009). Human age estimation using bio-inspired features. IEEE Proceedings Conference of Computer Vision and Pattern Recognition, 112–119.

  6. Abousaleh, F. S., Lim, T., Cheng, W.-H., Yu, N.-H., Hossain, M. A., & Alhamid, M. F. (December 2016). A novel comparative deep learning framework for facial age estimation. EURASIP Journal on Image and Video Processing.

  7. Choi, S. E., Lee, Y. J., Lee, S. J., Park, K. R., & Kim, J. (2011). Age estimation using a hierarchical classifier based on global and local facial features. Journal of Pattern Recognition, 44(6), 1262–1281.

    Article  Google Scholar 

  8. Chao, W.-L., Liu, J.-Z., & Ding, J.-J. (March 2013). Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognition, 46(3), 628–641.

    Article  Google Scholar 

  9. Huerta, I., Fernández, C., Segura, C., Hernando, J., & Prati, A. (December 2015). A deep analysis of age estimation. Pattern Recognition Letters, 68, 239–249.

    Article  Google Scholar 

  10. Mayer, F., Geserick, T. A. G., Grundmann, C., Lockemann, U., Riepert, T., Schmeling, A., & St. Ritz-Timme. (July 2015). Age estimation based on pictures and videos presumably showing a child or youth pornography—reply to Arlan L. Rosenbloom. International Journal of Legal Medicine, 129(4), 833–833.

    Article  Google Scholar 

  11. Kumar, S., Jayadevappa, D., & Bhopale, S. D. (April - 2014). Implementation of image segmentation using FPGA. International Journal of Engineering Research & Technology (IJERT), 3(4), 2700–2703.

    Google Scholar 

  12. Ninu Preetha, N. S., Brammya, G., Ramya, R., Praveena, S., Binu, D., & Rajakumar, B. R. (2018). Grey wolf optimisation-based feature selection and classification for facial emotion recognition. IET Biometrics, 7(5), 490–499.

    Article  Google Scholar 

  13. Yang, Z., & Ai, H. (2007). Demographic classification with local binary patterns. In Proceedings of the IEEE International Conference on Biometrics, 464–473.

  14. Gao, F., & Ai, H. (2009). Face age classification on consumer images with Gabor feature and fuzzy LDA method. In Proceedings of the IEEE International Conference Biometrics, 132–141.

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

    Article  Google Scholar 

  16. Geng, X., Zhou, Z.-H., & Smith-Miles, K. (2007). Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2234–2240.

    Article  Google Scholar 

  17. Li, K., Xing, J., Hu, W., & Maybank, S. J. (June 2017). D2C: Deep cumulatively and comparatively learning for human age estimation. Pattern Recognition, 66, 95–105.

    Article  Google Scholar 

  18. Fu, Y., & Huang, T. S. (2007). Human age estimation with regression on discriminative aging manifold. IEEE Transactions on Multimedia, 10(4), 578–584.

    Article  MathSciNet  Google Scholar 

  19. Guo, G., Fu, Y., Dyer, C., & Huang, T. S. (2008). Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing(TIP), 17(7), 1178–1188.

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  21. Fu, Y., Xu, Y., Huang, T.S. (2007) Estimating human age by manifold analysis of face pictures and regression on aging features. In Proceedings of the IEEE International Conference on Multimedia and Expo, Beijing, China, IEEE, pp.1383–1386.

  22. Tian, Q., & Chen, S. (2015). Cumulative attribute relation regularization learning for human age estimation. Neurocomputing, 165, 456–467.

    Article  Google Scholar 

  23. Lanitis, A., Draganova, C., & Christodoulou, C. (2004). Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(1), 621–628.

    Article  Google Scholar 

  24. Ueki, K., Hayashida, T., Kobayashi, T. (2006) Subspace-based age-group classification using facial images under various lighting conditions,” In Proceedings of the IEEE 7th International Conference on Automatic Face and Gesture Recognition, pp. 6–pp.

  25. Sai, P. K., Wang, J. G., & Teoh, E. K. (2015). Facial age range estimation with extreme learning machines. Neuro computing, 149, 364–372.

    Google Scholar 

  26. Thomas, R., & Rangachar, M. J. S. (2018). Hybrid optimization based DBN for face recognition using low-resolution images. Multimedia Research, 1(1), 33–43.

    Google Scholar 

  27. Ruikar, S. & V Chandra Prakash Ghuge C. A. (2016) Query-specific distance and hybrid tracking model for video object retrieval. Journal of Intelligent Systems, 27(2), 1-18

  28. Dong, Y., Liu, Y., & Lian, S. (2016). Automatic age estimation based on deep learning algorithm. Neurocomputing, 187, 4–10.

    Article  Google Scholar 

  29. Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70, 489–501.

    Article  Google Scholar 

  30. Sun, Z. L., Au, K. F., & Choi, T. M. (2007). A hybrid neuronfuzzy inference system through integration of fuzzy logic and extreme learning machines. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 37(5), 1321–1331.

    Article  Google Scholar 

  31. Zhan-Li Sun; Ru-Xia Ban; Chao Zheng; Tao Shen; Cheng-Gang Gu; Xiao-Feng Zhou (2017) An effective approach of facial age estimation with extreme learning machine. In Proceedings of the 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp.1146–1149.

  32. Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169, 1–12.

    Article  Google Scholar 

  33. Chang, K.-Y., & Chen, C.-S. (2015). A learning framework for age rank estimation based on face images with scattering transform. IEEE Transactions on Image Processing, 24(3), 785–798.

    Article  MathSciNet  Google Scholar 

  34. Liu, H., Lu, J., Feng, J., & Zhou, J. (June 2017). Group-aware deep feature learning for facial age estimation. Pattern Recognition, 66, 82–94.

    Article  Google Scholar 

  35. Tian, Q., & Chen, S. (2017). Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing, 238, 286–295.

    Article  Google Scholar 

  36. Li, W., Lu, J., Feng, J., Xu, C., Zhou, J., Tian, Q. (2019) BridgeNet: A continuity-aware probabilistic network for age estimation. The In the proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1145–1154.

  37. Zhang, C., Liu, S., Xu, X., Zhu, C. (2019) C3AE: Exploring the limits of compact model for age estimation, In the proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12587–12596.

  38. Ma, Y., Liu, J., Yang, X., Liu, Y., & Zheng, N. (2015). Double layer multiple tasks learning for age estimation with insufficient training samples. Neurocomputing, 147, 380–386.

    Article  Google Scholar 

  39. Sahoo, T. K., & Haider Banka. (March 2017). New hybrid PCA-based facial age estimation using inter-age group variation-based hierarchical classifier. Arabian Journal for Science and Engineering, 42(8), 3337–3355.

    Article  Google Scholar 

  40. Vaishnavi, P. K., & Pavitra, M. (2018). Face recognition using VIOLA-JONES algorithm. International Journal of Innovative Science and Research Technology, 3(2), 163–167.

    Google Scholar 

  41. Pandey, P., Singh, R., Vatsa, M. (2016) Face recognition using scattering wavelet under Illicit Drug Abuse variations. 2016 International Conference on Biometrics (ICB), Halmstad, pp. 1–6.

  42. Batur, A. U., & Hayes, M. H. (November 2005). Adaptive active appearance models. IEEE Transactions on Image Processing, 14(11), 1707–1721.

    Article  Google Scholar 

  43. IMDB face database from https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/, Accessed on 5 Aug 2017.

  44. Adience database form http://www.cslab.openu.ac.il/personal/Hassner/adiencedb/, Accessed on 5 Aug 2017.

  45. AFAD Dataset Online available at https://afad-dataset.github.io/, Accessed on 10 November 2020

  46. FG-NET Dataset Online available at http://yanweifu.github.io/FG_NET_data/FGNET.zip, Accessed on 10 November 2020

  47. Duan, M., Li, K., & Li, K. (March 2018) An ensemble CNN2ELM for age estimation. IEEE Transactions on Information Forensics and Security, 13(3).

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Jawaharlal Nehru Technological University Anantapur, Ananthapuramu, India

    Anjali A. Shejul

  2. Department of Electronics &Telecommunication, Pimpri Chinchwad College of Engineering, Pune, India

    Kishor S. Kinage

  3. Department of Computer Science & Engineering, JNTUA College of Engineering, Ananthapuramu, India

    B. Eswara Reddy

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  1. Anjali A. Shejul
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  2. Kishor S. Kinage
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  3. B. Eswara Reddy
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Correspondence to Anjali A. Shejul.

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Shejul, A.A., Kinage, K.S. & Reddy, B.E. CDBN: Crow Deep Belief Network Based on Scattering and AAM Features for Age Estimation. J Sign Process Syst 93, 879–897 (2021). https://doi.org/10.1007/s11265-020-01609-z

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