Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-8 Number-6                          
Year of Publication : 2014
Authors : Shireesha Chintalapati , M. V. Raghunadh
  10.14445/22315381/IJETT-V8P254

Citation 

Shireesha Chintalapati , M. V. Raghunadh."Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications", International Journal of Engineering Trends and Technology(IJETT), V8(6),292-298 February 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Face recognition in real-time scenarios is mainly affected by illumination, expression and pose variations and also by occlusion. This paper presents the framework for pose-adaptive component-based face recognition system. The framework proposed deals with all the above mentioned issues. The steps involved in the presented framework are (i) facial landmark localisation, (ii) facial component extraction, (iii) pre-processing of facial image (iv) facial pose estimation (v) feature extraction using Local Binary Pattern Histograms of each component followed by (vi) fusion of pose adaptive classification of components. By employing pose adaptive classification, the recognition process is carried out on some part of database, based on estimated pose, instead of applying the recognition process on the whole database. Pre-processing techniques employed to overcome the problems due to illumination variation are also discussed in this paper. Component-based techniques provide better recognition rates when face images are occluded compared to the holistic methods. Our method is simple, feasible and provides better results when compared to other holistic methods.

References

[1] Jain, Anil K and Li, Stan Z, Handbook of face recognition, Springer, 2005.
[2] Brunelli, R., Poggio, T, Face Recognition through Geometrical Features. European Conference on Computer Vision (ECCV) 1992, S. 792–800.
[3] Turk, M., and Pentland, A. Eigenfaces for recognition. Journal of Cognitive Neuroscience 3 (1991), 71–86.M.
[4] Belhumeur, P. N., Hespanha, J., and Kriegman, D. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711–720.
[5] Ahonen, T., Hadid, A., and Pietikainen, M., Face Recognition with Local Binary Patterns, Computer Vision - ECCV 2004 (2004), 469–481.
[6] Kathryn Bonnen, Brendan Klare, Anil Jain, Component based representation in automated face recognition, IEEE Transactions on Information Forensic and Security.
[7] Celiktutan, Oya and Ulukaya, Sezer and Sankur, B,A Comparitive study of face landmarking techniques, EURASIP Journal on Image and Video Processing, Springer,2013.
[8] Loconsole, Claudio and Barbosa, Nuno and Frisoli, Antonio and Orvalho, Veronica Costa, A new marker-less 3d kinect-based system for facial anthropometric measurements,Springer, 2012
[9] Biomedical Imaging Group website. [Online]. Available: http://bigwww.epfl.ch/sage/soft/localnormalization/
[10] Microsoft Developer Network website. [Online]. Available: http://msdn.microsoft.com/en-us/library/jj130970.aspx
[11] Rameez Qasim, M. Mutsaied Shirazi, Naveel Arshad, Ikram Qureshi, Sajjad Zaidi, Comparison and Improvement of PCA and LBP Efficiency for Face Recognition. IEEE,2013.
[12] Alsaade, Fawaz and Zahrani, Mohammed and Alghamdi, Turki, “Score-Level Fusion in Biometric Verification,” International Symposium on Biometrics and Security Technologies (ISBAST), IEEE,2013
[13] Cao, Zhimin and Yin, Qi and Tang, Xiaoou and Sun, Jian, “Face recognition with learning-based descriptor,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

Keywords
Pose estimation, Component extraction, LBPH, Kinect.