A Robust Face Recognition System for One Sample Problem

  • Mahendra Singh Meena
  • Priti Singh
  • Ajay Rana
  • Domingo Mery
  • Mukesh PrasadEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11854)


Most of the practical applications have limited number of image samples of individuals for face verification and recognition process such as passport, driving licenses, photo ID etc. So use of computer system becomes challenging task, when image samples available per person for training and testing of system are limited. We are proposing a robust face recognition system based on Tetrolet, Local Directional Pattern (LDP) and Cat Swam Optimization (CSO) to solve this problem. Initially, the input image is pre-processed to extract region of interest using filtering method. This image is then given to the proposed descriptor, namely Tetrolet-LDP to extract the features of the image. The features are subjected to classification using the proposed classification module, called Cat Swarm Optimization based 2-Dimensional Hidden Markov Model (CSO-based 2DHMM) in which the CSO trains the 2D-HMM. The performance is analyzed using the metrics, such as accuracy, False Rejection Rate (FRR), & False Acceptance Rate (FAR) and the system achieves high accuracy of 99.65%, and less FRR and FAR of 0.0033 and 0.003 for training percentage variation and 99.65%, 0.0035 and 0.004 for k-Fold Validation.


Face recognition Tetrolet Local Directional Pattern (LDP) Cat Swarm Optimization (CSO) 2-Dimensional Hidden Markov Model (2DHMM) k-fold validation Training percentage 


  1. 1.
    Mian, A., Bennamoun, M., Owens, R.: Key-point detection and local feature matching for textured 3D face recognition. Int. J. Comput. Vision 79(1), 112 (2008)CrossRefGoogle Scholar
  2. 2.
    Bennamoun, M., Guo, Y., Sohel, F.: Feature selection for 2D and 3D face recognition. In: Encyclopedia of Electrical and Electronics Engineering, p. 154. Wiley (2015)Google Scholar
  3. 3.
    Mian, A., Bennamoun, M., Owens, R.: An efficient multimodal 2D–3D hybrid approach to automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1927–1943 (2007)CrossRefGoogle Scholar
  4. 4.
    Berretti, S., Werghi, N., del Bimbo, A., Pala, P.: Selecting stable key-points and local descriptors for person identification using 3D face scans. Vis. Comput. 30(11), 1275–1292 (2014)CrossRefGoogle Scholar
  5. 5.
    Wong, H.S., Cheung, K., Ip, H.: 3D head model classification by evolutionary optimization of the extended Gaussian Image representation. Pattern Recogn. 37(12), 2307–2322 (2004)CrossRefGoogle Scholar
  6. 6.
    Liu, P., Wang, Y., Huang, D., Zhang, Z., Chen, L.: Learning the spherical harmonic features for 3-D face recognition. IEEE Trans. Image Process. 22(3), 914–925 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Expression invariant representations of faces. IEEE Trans. Image Process. 16(1), 188–197 (2007)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Zaman, F.K., Shafie, A.A., Mustafah, Y.M.: Robust face recognition against expressions and partial occlusions. Int. J. Autom. Comput. 13(4), 319–337 (2016)CrossRefGoogle Scholar
  9. 9.
    Guo, Y., Lei, Y., Liu, L., Wang, Y., Bennamoune, M., Sohel, F.: EI3D: expression-invariant 3D face recognition based on feature and shape matching. Pattern Recogn. Lett. 83, 403–412 (2016)CrossRefGoogle Scholar
  10. 10.
    Zhao, W., Chellappa, R., Philips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar
  11. 11.
    Wu, J., Zhou, Z.H.: Face recognition with one training image per person. Pattern Recogn. Lett. 23(2), 1711–1719 (2001)zbMATHGoogle Scholar
  12. 12.
    Jung, H.C., Hwang, B.W., Lee, S.W.: Authenticating corrupted face image based on noise model. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, vol. 272 (2004)Google Scholar
  13. 13.
    De la Torre, F., Gross, R., Baker, S., Kumar, V.: Representational oriented component analysis (ROCA) for face recognition with one sample image per training class. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 266–273 (2005)Google Scholar
  14. 14.
    Martinez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 748–763 (2002)CrossRefGoogle Scholar
  15. 15.
    A Gentle Introduction to \(k\)-fold Cross-Validation. Accessed 16 April 2019
  16. 16.
    Jabid, T., Hasanul, K.Md., Chae, O.: Local Directional Pattern (LDP) for face recognition. In: Proceedings of the Digest of Technical Papers International Conference on Consumer Electronics (ICCE), pp. 329–330 (2010)Google Scholar
  17. 17.
    Krommweh, J.: Tetrolet transform: a new adaptive Haar wavelet algorithm for sparse image representation. J. Vis. Commun. Image Represent. 21(4), 364–374 (2010)CrossRefGoogle Scholar
  18. 18.
    Chu, S.-C., Tsai, P., Pan, J.-S.: Cat swarm optimization. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 854–858. Springer, Heidelberg (2006). Scholar
  19. 19.
    Collection of Facial Images. Accessed 12 Apr 2019
  20. 20.
    CVL face database. Accessed 27 Aug 2018
  21. 21.
    Chihaoui, M., Bellil, W., Elkefi, A., Amar, C.B.: Face recognition using HMM-LBP. In: Abraham, A., Han, S.Y., Al-Sharhan, S.A., Liu, H. (eds.) Hybrid Intelligent Systems. AISC, vol. 420, pp. 249–258. Springer, Cham (2016). Scholar
  22. 22.
    Bevilacqua, V., Cariello, L., Carro, G., Daleno, D., Mastronardi, G.: A face recognition system based on Pseudo 2D HMM applied to neural network coefficients. Soft Comput. 12(7), 615–621 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahendra Singh Meena
    • 1
  • Priti Singh
    • 2
  • Ajay Rana
    • 3
  • Domingo Mery
    • 4
  • Mukesh Prasad
    • 1
    Email author
  1. 1.School of Computer Science, FEITUniversity of Technology SydneyUltimo, SydneyAustralia
  2. 2.Amity University HaryanaGurgaonIndia
  3. 3.Amity University Utter PradeshNoidaIndia
  4. 4.Department of Computer ScienceUniversity of ChileSantiagoChile

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