Multimedia Tools and Applications

, Volume 76, Issue 3, pp 3751–3760 | Cite as

An efficient approach for face recognition in uncontrolled environment

Article

Abstract

There is a great demand of automatic face recognition in the society. The methods of face recognition are performed satisfactorily in controlled environment. The challenging benchmarks demonstrate that these methods may not adequately work in unconstrained environment. In this paper, we develop a novel framework of face recognition system that outperforms in unconstrained environment. The framework works on features based method that extracts facial landmarks from images. After quality check the patch experts are generated and used to model the appearance of landmarks of interests. The effect of discriminatory features is further enhanced by assigning weights to them that are to be set to the ratio of the interclass variance to the intraclass variance. The result shows that the proposed framework achieves better recognition accuracy in comparison to other known methods on publically available challenging datasets.

Keywords

Face recognition Facial features detection and unconstrained environment 

References

  1. 1.
    Baltrusaitis T, Robinson P and Morency L-P (2013) Constrained local neural fields for robust facial landmark detection in the wild, pp. 354–361, ICCVGoogle Scholar
  2. 2.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  3. 3.
    Chevallier L, Vigouroux J, Goguey A, Ozerov A (2013) Facial landmarks localization estimation by cascaded boosted regression. Computer Vision, Imaging and Computer Graphics -- Theory and Applications, CCIS Springer 458:103–115Google Scholar
  4. 4.
    Cristinacce D and Cootes T (2006) Feature detection and tracking with constrained local models. In BMVCGoogle Scholar
  5. 5.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. International Conference on Computer Vision & Pattern Recognition, Vol. 2, INRIA Rhone-Alpes, ZIRST-655, av. de l’Europe, Montbonnot-38334, pp. 886–893Google Scholar
  6. 6.
    Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11:467–476CrossRefGoogle Scholar
  7. 7.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  8. 8.
    Markuš N, Frljak M, Pandzic IS, Ahlberg J, Forchheimer R (2014) Fast localization of facial landmark points, CoRR, abs/1403.6888. URL http://arxiv.org/abs/1403.6888
  9. 9.
    Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  10. 10.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefMATHGoogle Scholar
  11. 11.
    Shyam R and Singh YN (2014) A taxonomy of 2D and 3D face recognition methods. In: Proc. of 1st Int’l Conf. on Signal Processing and Integrated Networks (SPIN 2014), pp. 749–754, IEEEGoogle Scholar
  12. 12.
    Shyam R and Singh YN (2015) Face recognition using augmented local binary patterns and bray curtis dissimilarity metric. In: Proc. of 2nd Int’l Conf. on Signal Processing and Integrated Networks (SPIN 2015), pp. 779–784, IEEEGoogle Scholar
  13. 13.
    Shyam R and Singh YN (2015) Recognizing individuals from unconstrained facial images. In: Proc. of Advances in Intelligent Systems and Computing Series (AISC), Springer, vol. 384, (Switzerland), pp. 383–392Google Scholar
  14. 14.
    Shyam R and Singh YN (2016) Multialgorithmic frameworks for human face recognition. J Electr Comput Eng 2016(2016):1–9Google Scholar
  15. 15.
    Singh YN (2015) Human recognition using fisher’s discriminant analysis of heartbeat interval features and ECG morphology. Neurocomputing 167:322–335CrossRefGoogle Scholar
  16. 16.
    The Database of face94, face95 and face96, Spacek DL (2012) Face recognition data, University of Essex. UK. Computer Vision Science Research ProjectsGoogle Scholar
  17. 17.
    Turk MA, Pentland AP (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  18. 18.
    Zhang Z, Luo P, Loy CC, Tang X (2014) Facial landmark detection by deep multi-task learning. European Conference on Computer Vision, 94–108Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Apollo Institute of TechnologyKanpurIndia
  2. 2.Institute of Engineering & Technology, Dr APJ Abdul Kalam Technical UniversityUttar PradeshLucknowIndia

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