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An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Feature Transform
CH. Hima Bindu, K. Manjunathachari
Pages - 39 - 47     |    Revised - 31-05-2018     |    Published - 30-06-2018
Volume - 12   Issue - 2    |    Publication Date - June 2018  Table of Contents
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KEYWORDS
SIFT, SVM, Multi-kernel SIFT, Face Recognition.
ABSTRACT
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
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1 He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang. "Face recognition using Laplacian faces". IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3):328-340, 2005..
2 E. Makinen, R. Raisamo, Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541-547, 2008
3 Y. Shinohara and N. Otsu, "Facial Expression Recognition Using Fisher Weight Maps," in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Vol.100, pp.499-504, 2004.
4 R. Chellappa, C. L. Wilson and S. Sirohey, "Human and machine recognition of faces: a survey", Proceedings of the IEEE, Vol. 83, No. 5,705-740, 1995
5 Lowe, D.G. "Object recognition from local scale invariant features". In Proceedings of the 7th IEEE International Conference on Computer Vision, IEEE, Vol. 2, 1150-1157, 20-27 September, 1999
6 Lowe, D.G. "Distinctive image features from scale-invariant keypoints". International Journal of Computer Vision, 60 (2), 91-110, 2004
7 Ke, Y., Sukthankar, R.. "PCA-SIFT: A more distinctive representation for local image descriptors". In Computer Vision and Pattern Recognition (CVPR 2004), 27 June - 2 July 2004. IEEE, Vol. 2, 506-513.
8 Bay, H., Tuytelaars, T., Gool, L.V. "SURF: Speeded up robust features". In Computer Vision - ECCV 2006 : 9th European Conference on Computer Vision, 7-13 May 2006. Springer, Part II, 404-417.
9 F. Alhwarin, C. Wang, D. Ristic-Durrant, and A. Graser, "Improved SIFT-features matching for object recognition," in Proceedings of the International Conference on Visions of Computer Science: BCS International Academic Conference (VoCS '08), pp. 178-190, London, UK.
10 S. Saleem and R. Sablatnig, "A modified SIFT descriptor for image matching under spectral variations," in Image Analysis and Processing-ICIAP 2013: 17th International Conference, Naples, Italy, September 9-13, 2013. Proceedings, Part I, vol. 8156 of Lecture Notes in Computer Science, pp. 652-661, Springer, Berlin, Germany, 2013.
11 A. T. Tra, W. Lin, and A. Kot, "Dominant SIFT : A Novel Compact Descriptor," in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, May 2015.
12 A. Mohamed, "Face recognition using SIFT features," Tech. Rep., Caltech, Pasadena, Calif, USA,2006.
13 H. Yanbin, Y. Jianqin, and L. Jinping, "Human face feature extraction and recognition base on SIFT," in Proceedings of the International Symposium on Computer Science and Computational Technology (ISCSCT '08), vol. 1, pp. 719-722, Shanghai, China, December 2008.
14 Geng, Cong, and Xudong Jiang. "Face recognition using SIFT features." In Image Processing (ICIP), 2009 16th IEEE International Conference on, pp. 3313-3316. IEEE, 2009.
15 A. Majumdar and R. K. Ward, "Discriminative SIFT features for face recognition," in Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE '09), pp. 27-30, St. John's, Canada, May 2009.
16 J. Križaj, V. Štruc, and N. Pavešić, "Adaptation of SIFT features for robust face recognition," in Image Analysis and Recognition: 7th International Conference, ICIAR 2010, Póvoa de Varzim, Portugal, June 21-23, 2010. Proceedings, Part I, vol. 6111 of Lecture Notes in Computer Science, pp. 394-404, Springer, Berlin, Germany, 2010.
17 I. Dagher, N. El Sallak, and H. Hazim, "Face recognition using the most representative SIFT images," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 7, no. 1, pp. 225-236, 2014.
18 L. Lenc and P. Kral, "Automatic face recognition system based on the SIFT features".
19 Peter Peer, CVL Face Database, http://www.lrv.fri.uni-lj.si/facedb.html.
Mrs. CH. Hima Bindu
Dept. of ECE, GITAM University, Hyderabad, INDIA - India
himabindu.chelluri@gmail.com
Mr. K. Manjunathachari
Dept. of ECE GITAM University Hyderabad, INDIA - India