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Estimation of Human Age and Gender Based on LBP Features Using Two Level Decision by SVM

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1380))

Abstract

Automated face and age recognition is becoming an important technology due to the huge amount of biometric transactions will be carried and processed daily by Authorization agencies. Now a days Estimation of human age is a very significant characteristic in terms of identity authentication. In order to assess one’s individual age, selection of features from the human face is significant and vital with the perspective of experimental accuracy. Some of the Real world Age estimation applications are Biometrics, Security management, 3D face construction and cosmetology. Statistical and regression based are the traditional and earlier techniques commonly used age estimation technique which works on results of interrelationship and correlation among the different values of age for face images. The current work is an enhancement of existing techniques used previously for feature extraction using Local binary patterns with regional features of 2D Wavelet transformation image and age estimation can be accomplished by using SVM Classifier.

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References

  1. Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation, IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 621–628 (2004)

    Google Scholar 

  2. Lin, C.-T., Li, D.-L., Lai, J.-H., Han, M.-F., Chang, J.-Y.: Automatic age estimation system for face images. Int. J. Adv. Robot. Syst. 216 (2012). https://doi.org/10.5772/52862

  3. Nguyen, D.T., Cho, S.R., Shin, K.Y., Bang, J.W., Park, K.R.: Comparative study of human age estimation with or without pre classification of gender and facial expression. Sci. World J. 905269, 15 p. (2014). https://doi.org/10.1155/2014/905269

  4. Parmar, D.N., Mehta, B.B.: Face recognition methods & applications. Int. J. Comput. Technol. Appl. (IJCTA) 4(1), 84–86 (2013). ISSN 2229-6093

    Google Scholar 

  5. Ravichandran, D., Nimmatoori, R., Gulam Ahamad, M.: Mathematical representations of 1D, 2D and 3D wavelet transform for image coding. Int. J. Adv. Comput. Theory Engineering (IJACTE) 5(3) (2016). ISSN (Print): 2319-2526

    Google Scholar 

  6. Liao, H., Yan, Y., Dai, W., Fan, P.: Age estimation of face images based on CNN and divide-and-rule strategy. Math. Probl. Eng. 2018, Article ID 1712686, 8 p. (2018). https://doi.org/10.1155/2018/1712686

  7. Ahmed, M., Laskar, R.H.: Eye center localization in a facial image based on geometric shapes of iris and eyelid under natural variability. Image Vis. Comput. 88, 52–66 (2019). https://doi.org/10.1016/j.imavis.2019.05.002 0262-8856/Elsevier

  8. Kafai, M., An, L., Bhanu, B.: Reference face graph for face recognition. IEEE Trans. Inf. Forensics Secur. 9(12), 2132–2143 (2014). https://doi.org/10.1109/TIFS.2014.2359548

    Article  Google Scholar 

  9. Sukhija, P., Behal, S., Singh, P.: Face recognition system using genetic algorithm. In: International Conference on Computational Modeling and Security (CMS 2016), Published by Elsevier B.V. an open access article under the CC BY-NC-ND license (2016). http://creativecommons.org/licenses/by-nc-nd/4.0/. 1877-0509

  10. Raghavendra, S.P., Danti, A.: A novel recognition if Indian bank cheque names using binary pattern and feed forward neural network. IOSR J. Comput. Eng. (IOSR-JCE) 20(3), Ver I, 44–59 (2018). UGC Approved Journal (Jr. No. 5019). e-ISSN: 2278-0661, p-ISSN: 2278-8727

    Google Scholar 

  11. Raghavendra, S.P., Danti, A.: Recognition of signature using neural network and Euclidean distance for bank cheque automation. In: Santosh, K., Hegadi, R. (eds.) Recent Trends in Image Processing and Pattern RecognitionInternational Conference on Recent Trends in image Processing (RTIP2R) 2018 Solapur, Communication in Computer and Information Science (CCIS), vol. 1037, pp. 228–243. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-9187-2_21

  12. Ranjan, R., et al.: A fast and accurate system for face detection, identification, and verification. J. Latex Class Files 14(8) (2015)

    Google Scholar 

  13. Angulu, R., Tapamo, J.R., Adewumi, A.O.: Age estimation via face images: a survey. EURASIP J. Image Video Process. 2018, 42 (2018). https://doi.org/10.1186/s13640-018-0278-6

  14. Atallah, R.R., Kamsin, A., Ismail, M.A., Abdelrahman, S.A., Zerdoumi, S.: Face recognition and age estimation implications of changes in facial features: a critical review study. IEEE Access 6, 28290–28304 (2018). https://doi.org/10.1109/ACCESS.2018.2836924

    Article  Google Scholar 

  15. Arya, S., Pratap, N., Bhatia, K.: Future of face recognition: a review. Procedia Comput. Sci. 58(2015), 578–585 (2015). In: Second International Symposium on Computer Vision and the Internet (VisionNet 2015). Elsevier

    Article  Google Scholar 

  16. Verma, V.P., Verma, D.: A survey on facial age estimation techniques. IJSE Int. J. Comput. Sci. Eng. 6(8) (2018). E-ISSN: 2347-2693. Open Access Survey Paper

    Google Scholar 

  17. Manjula, V.S., Baboo, S.S.: Face detection identification and tracking by PRDIT algorithm using image database for crime investigation. Int. J. Comput. Appl. (0975-8887) 38(10) (2012)

    Google Scholar 

  18. Trehan, U., Awasthi, A.K., Gupta, S., Singh, P.: Security authentication system using facial recognition. J. Netw. Commun. Emerg. Technol. (JNCET) 8(4) (2018). http://www.jncet.org

  19. Geng, X., Zhou, Z.H., Zhang, Y., Li, G., Dai, H.: Learning from facial aging patterns for automatic age estimation. In: MM 2006, Santa Barbara, California, USA, 23–27 October, Copyright 2006 ACM (2006). 1-59593-447-2/06/0010 ...\$5.00

    Google Scholar 

  20. Yadav, D., Singh, R., Vatsa, M., Noore, A.: Recognizing age separated face images: humans and machines. PLoS ONE 9(12), e112234 (2014). https://doi.org/10.1371/journal.pone.0112234

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of College of Engineering, JNN College of Engineering, Shimoga, Karnataka, India

    S. P. Raghavendra & M. J. Adarsh

  2. Department of Computer Science, GFGC, Sorab(T), Shimoga, Karnataka, India

    Shoieb Ahamed

  3. Siemens Technology and Services India Pvt Ltd., Bangalore, Karnataka, India

    J. Shree Hari

Authors
  1. S. P. Raghavendra
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  2. M. J. Adarsh
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  3. Shoieb Ahamed
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  4. J. Shree Hari
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Corresponding author

Correspondence to S. P. Raghavendra .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India

    Bharti Gawali

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Raghavendra, S.P., Adarsh, M.J., Ahamed, S., Shree Hari, J. (2021). Estimation of Human Age and Gender Based on LBP Features Using Two Level Decision by SVM. In: Santosh, K.C., Gawali, B. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2020. Communications in Computer and Information Science, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-0507-9_8

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  • DOI: https://doi.org/10.1007/978-981-16-0507-9_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0506-2

  • Online ISBN: 978-981-16-0507-9

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