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Deep Component Based Age Invariant Face Recognition in an Unconstrained Environment

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Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

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Abstract

Age Invariant face recognition is one of the challenging problems in pattern recognition. Most existing face recognition algorithms perform well under controlled conditions where the data set is collected with careful cooperation with the individuals. However, in most real-world applications, the user usually has little or no control over environmental conditions. This paper proposes efficient deep component-based age-invariant face recognition algorithm in an unconstrained environment. The algorithm detects face from an image, align the face and extract the facial components (eye, mouth and nose). Each facial component is then trained using deep neural network. Thus, deep features are extracted from each component. Support vector machine is then used in classification stage. Experiments are conducted on two challenging benchmarks: AgeDb30 and Pins-Face. Results have shown significant improvement when compared with the state of the art baseline approach.

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References

  1. Al-Ghamdi, B.A.S.: Recognition of human face by face recognition system using 3D. J. Inf. Commun. Technol. (JICT) 4(2), 8 (2010)

    Google Scholar 

  2. Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), pp. 1–6. IEEE (2017)

    Google Scholar 

  3. Choraś, M.: The lip as a biometric. Pattern Anal. Appl. 13(1), 105–112 (2010). https://doi.org/10.1007/s10044-008-0144-8

    Article  MathSciNet  Google Scholar 

  4. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  5. Du, S., Ward, R.K.: Adaptive region-based image enhancement method for robust face recognition under variable illumination conditions. IEEE Trans. Circ. Syst. Video Technol. 20(9), 1165–1175 (2010)

    Article  Google Scholar 

  6. Gold, J.M., et al.: The perception of a familiar face is no more than the sum of its parts. Psychon. Bull. Rev. 21(6), 1465–1472 (2014). https://doi.org/10.3758/s13423-014-0632-3

    Article  Google Scholar 

  7. Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: MS-Celeb-1M: a dataset and benchmark for large-scale face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 87–102. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_6

    Chapter  Google Scholar 

  8. Hassan, A., Viriri, S.: Invariant feature extraction for component-based facial recognition. Int. J. Adv. Comput. Sci. Appl. (2020)

    Google Scholar 

  9. Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: global versus component-based approach. In: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 688–694. IEEE (2001)

    Google Scholar 

  10. Heisele, B., Koshizen, T.: Components for face recognition. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings, pp. 153–158. IEEE (2004)

    Google Scholar 

  11. Kute, R.S., Vyas, V., Anuse, A.: Component-based face recognition under transfer learning for forensic applications. Inf. Sci. 476, 176–191 (2019)

    Article  Google Scholar 

  12. Mo, N., Yan, L., Zhu, R., Xie, H.: Class-specific anchor based and context-guided multi-class object detection in high resolution remote sensing imagery with a convolutional neural network. Remote Sens. 11(3), 272 (2019)

    Article  Google Scholar 

  13. Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: AgeDB: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51–59 (2017)

    Google Scholar 

  14. Nixon, M.S., Bouchrika, I., Arbab-Zavar, B., Carter, J.N.: On use of biometrics in forensics: gait and ear. In: 2010 18th European Signal Processing Conference, pp. 1655–1659. IEEE (2010)

    Google Scholar 

  15. Paul, S.K., Uddin, M.S., Bouakaz, S.: Face recognition using eyes, nostrils and mouth features. In: 16th International Conference on Computer and Information Technology, pp. 117–120. IEEE (2014)

    Google Scholar 

  16. Radji, N., Cherifi, D., Azrar, A.: Importance of eyes and eyebrows for face recognition system. In: 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), pp. 1–6. IEEE (2015)

    Google Scholar 

  17. Raj, S., Kumar, S., Raj, S.: An improved histogram equalization technique for image contrast enhancement, January 2015. ResearchGate

    Google Scholar 

  18. Sellahewa, H., Jassim, S.: Face recognition in the presence of expression and/or illumination variation. In: Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID 2005), pp. 144–148. IEEE (2005)

    Google Scholar 

  19. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)

    Google Scholar 

  20. Wang, Y.Q.: An analysis of the viola-jones face detection algorithm. Image Process. On Line 4, 128–148 (2014)

    Article  Google Scholar 

  21. Zhang, Q., et al.: VarGNet: variable group convolutional neural network for efficient embedded computing. arXiv preprint arXiv:1907.05653 (2019)

  22. Zhou, E., Cao, Z., Yin, Q.: Naive-deep face recognition: touching the limit of LFW benchmark or not? arXiv preprint arXiv:1501.04690 (2015)

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Acknowledgment

This work was supported in part by the Higher Education Commission (HEC) Pakistan, and in part by the Ministry of Planning Development and Reforms under the National Center in Big Data and Cloud Computing.

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Authors and Affiliations

  1. FAST School of Computing, National University of Computer and Emerging Sciences, Karachi Campus, Islamabad, Pakistan

    Amad Asif, Muhammad Atif Tahir & Mohsin Ali

Authors
  1. Amad Asif
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  2. Muhammad Atif Tahir
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  3. Mohsin Ali
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Corresponding authors

Correspondence to Muhammad Atif Tahir or Mohsin Ali .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

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Asif, A., Tahir, M.A., Ali, M. (2021). Deep Component Based Age Invariant Face Recognition in an Unconstrained Environment. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-88113-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88112-2

  • Online ISBN: 978-3-030-88113-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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