Analysis of Local Descriptors for Human Face Recognition

  • Radhey Shyam
  • Yogendra Narain Singh
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)


Facial image analysis is an important and profound research in the field of computer vision. The prime issue of the face recognition is to develop the robust descriptors that discriminate facial features. In recent years, the local binary pattern (LBP) has attained a big attention of the biometric researchers, for facial image analysis due to its robustness shown for the challenging databases. This paper presents a novel method for facial image representation using local binary pattern, called augmented local binary pattern (A-LBP) which works on the consolidation of the principle of locality of uniform and non-uniform patterns. It replaces the non-uniform patterns with the mod value of the uniform patterns that are consolidated with the neighboring uniform patterns and extract pertinent information from the local descriptors. The experimental results prove the efficacy of the proposed method over LBP on the publicly available face databases, such as AT & T-ORL, extended Yale B, and Yale A.


Face recognition Local binary pattern Histogram Descriptor 



The authors acknowledge the Institute of Engineering and Technology (IET), Lucknow, Uttar Pradesh Technical University (UPTU), Lucknow for their financial support to carry out this research under the Technical Education Quality Improvement Programme (TEQIP-II) grant.


  1. 1.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Lu, J., Kostantinos, N.P., Anastasios, N.V.: Face recognition using LDA-based algorithms. IEEE Trans. Neural Networks 14(1), 195–200 (2003)CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kiregman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Shyam, R., Singh, Y.N.: Evaluation of Eigenfaces and Fisherfaces using Bray Curtis Dissimilarity Metric. In: Proceedings of 9th IEEE International Conference on Industrial and Information Systems (ICIIS 2014), pp. 1–6 (2014)Google Scholar
  5. 5.
    Shyam, R., Singh, Y.N.: Identifying individuals using multimodal face recognition techniques. In: Proceedings of International Conference on Intelligent Computing, Communication & Convergence (ICCC-2014). TBA, Elsevier (2014)Google Scholar
  6. 6.
    Shyam, R., Singh, Y.N.: A taxonomy of 2D and 3D face recognition methods. In: Proceedings of 1st International Conference on Signal Processing and Integrated Networks (SPIN 2014), pp. 749–754. IEEE (2014)Google Scholar
  7. 7.
    Liao, S., Law, M.W.K., Chung, A.C.S.: Dominant local binary patterns for texture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Heikkila, M., Pietikainen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.: Face detection based on multiblock LBP representation. In: Proceedings of International Conference on Biometrics. (2007)Google Scholar
  10. 10.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Proceedings of 3rd International Workshop on Analysis and Modelling of Faces and Gestures. Lecture Notes in Computer Science (LNCS), vol. 4778, pp. 168–182. Springer (2007)Google Scholar
  11. 11.
    Wolf, L., Hassner, T., Taigman, Y.: Descriptor based methods in the wild. In: Proceedings of Workshop Faces in Real-Life Images: Detection, Alignment, and Recognition, Marseille, France (2008).
  12. 12.
    Pietikainen, M., Hadid, A., Zaho, G., Ahonen, T.: Computer vision using local binary patterns. In: Proceedings of Computational Imaging and Vision vol. 40, pp. 13–43. Springer (2011).
  13. 13.
    Shyam, R., Singh, Y.N.: Face recognition using augmented local binary patterns in unconstrained environments. In: Proceedings of 8th IAPR/IEEE International Conference on Biometrics (ICB 2015). TBA, Phuket, Thailand (2015)Google Scholar
  14. 14.
    Shyam, R., Singh, Y.N.: Face recognition using augmented local binary patterns and bray curtis dissimilarity metric. In: Proceedings of 2nd International Conference on Signal Processing and Integrated Networks (SPIN 2015). TBA, IEEE (2015)Google Scholar
  15. 15.
    Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota, FL (1994)Google Scholar
  16. 16.
    Lee, K.C., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)CrossRefGoogle Scholar
  17. 17.

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringInstitute of Engineering and TechnologyLucknowIndia

Personalised recommendations