International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 8, No. 4, 8 Apr. 2016
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GCSTLPP: Face Recognition using Gabor Center-Symmetric Tensor Locality Preservative Projection Approach in Video
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Author(s)
Index Terms
Face Recognition, Gabor Filter, Center Symmetric Local binary pattern (LBP), Tensor locality preserving projections (TLPP)
Abstract
Face Recognition has become the challenging and interesting research topic in the last few years. The aim is to design a robust Face Recognition System under different environmental conditions like illumination, pose and occlusion. These are the three major challenges in Face Recognition which may hinder the Face Recognition system. By combining the three successful representations such as Gabor filters, CS-LBP and TLPP better performance can be achieved as compared to just considering them individually. CS-LBP is used for describing interest regions which have good tolerance to illumination and computational efficiency and TLPP is used to take the data directly in the form of tensors as input. Since the number of the combined feature sets are more only a few feature sets is selected to be trained by the Support Vector Machine classifier. A number of experiments are conducted using YouTube celebrity, McGill Face dataset and as well as the own collected sequence under different conditions such as illumination variations, different poses, occlusion including indoor and outdoor scenes. This approach provides better results compared to traditional approaches.
Cite This Paper
Rajeshwari.J, K. Karibasappa, Gopalkrishna M.T, "GCSTLPP: Face Recognition using Gabor Center-Symmetric Tensor Locality Preservative Projection Approach in Video", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.4, pp.18-24, 2016. DOI:10.5815/ijmecs.2016.04.03
Reference
[1]Rajeshwari J, Veena H L , Dr. K. Karibasappa, Video Based Face Recognition Using Ga- bor Features and LBP under Varying Illumination or Pose, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 6, June 2015
[2]Divyarajsinh N. Parmar, Brijesh B. Mehta, Face Recognition Methods Applications, Int.J. Computer Technology Applications, Vol 4 (1),84-86, IJCTA — Jan-Feb 2013
[3]Xiaoyang Tan and Bill Triggs, Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition, AMFG 2007, LNCS 4778, pp. 235-249, 2007. Springer-Verlag Berlin Heidelberg 2007
[4]Rajeshwari J, ReeniyaBoopaiah, K.Karibasappa, ,Video Based Face Recognition with Gabor Features and Locality Preserving Projections under Varying Partial Occlusion, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 6, June 2015
[5]Lee T.S, Image Representation Using 2D Gabor Wavelets, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol 18, No. 10, pp.959-971, Oct. 1996.
[6]Ojala T, Pietikainen M, and Harwood D, A comparative study of texture measures with classification based on feature distributions, Pattern Recognition, Vol 29, No. 1, pp.51-59, January 1996.
[7]K. Srinivasa Reddy, V. Vijaya Kumar, B. Eswara Reddy, Face Recognition Based on Texture Features using Local Ternary Pattern, I.J. Image, Graphics and Signal Processing, 2015, 10, 37-46, Published Online September 2015, in MECS.
[8]DeepthyBoban, Cinita Mary Mathew, Detecting Surgically Altered Face Images Using CS-LBP and Genetic Algorithm, International Journal of Advanced Research in Com- puter Science and Software Engineering Volume 4, Issue 8, August 2014.
[9]Jain A, Nandakumar, K, Ross A, Score normalization in multimodal biometric systems Pattern Recognition 38(12), 2270-2285 (2005).
[10]Kittler J, Hatef, M., Duin, R.P, Matas, J, on combining classifiers, IEEE TPAMI 20(3), 226-239 (1998)
[11]Quan-You Zhao, Bao-Chang Pan, Jian-Jia Pan, Yuan-Yan Tang, Facial Expression Recognition Based On Fusion Of Gabor And LBP Features, Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, 30- 31 Aug. 2008
[12]Loris Nanni, Alessandra Lumini, Random interest regions for object recognition based on texture descriptors and bag of features, ARTICLE in Expert Systems with Applications January 2012
[13]DazhaoZheng, XiufengDu, Limin Cui, Tensor Locality Preserving Projections for Face Recognition, Systems Man and Cybernetics (SMC), IEEE International Conference, 2010
[14]M.T.Gopalakrishna,M, Ravishankar,D, R.Rameshbabu, Ten-LoPP: Tensor Locality Pre- serving Projections Approach for Moving Object Detection and Tracking , The 9th Inter- national Conference on Computing and Information Technology (IC2IT2013) Advances in Intelligent Systems and Computing Volume 209, 2013, pp 291-300
[15]M. Kim, S. Kumar, V. Pavlovic, and H. Rowley, Face Tracking and Recognition with Visual Constraints in Real-World Videos, Proc. 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1 8, 2008
[16]M. Demirkus, J. J. Clark and T. Arbel, Robust Semi-Automatic Head Pose Labeling for Real-World Face Video Sequences, Multimedia Tools and Applications, January 2013
[17]Turk M, Pentland A: Eigenfaces for Recognition. Cogn.Neurosci. 3(1), 71-86 (1991)
[18]Xiaofei He and ParthaNiyogi, Locality preserving projections, In Proc. of NIPS., Department of Computer Science, University of Chicago, Chicago,2003
[19]D.Cai, X.He and J.W.Han, Orthogonal laplacian faces for face recognition, IEEE Trans. on Image Procession , vol. 15, no. 11, 2006.
[20]Liu C, Wechsler H, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, IEEE Transactions on Image Processing, 11 (2002) 467-476.
[21]Ojala T, Pietikainen M, Maenpaa T, Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE TPAMI 24(7), 971-987 (2002)
[22]Ahonen T, Hadid A, Pietikainen M, Face description with local binary patterns: Application to face recognition, IEEE TPAMI 28(12) (2006)
[23]Heikkila Marko, PietikainenMatti, SchmidCordelia, Description of interest regions with center-symmetric local binary patterns, 5th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2006. Madurai, Inde, December, 2006. Lecture Notes in Computer Science volume 4338, pages 58–69. 2006.