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Study of Gabor Wavelet for Face Recognition Invariant to Pose and Orientation

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Book cover Proceedings of the International Conference on Soft Computing Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 397))

Abstract

Gabor filters have achieved enormous success in texture analysis, feature extraction, segmentation, iris and face recognition. Face recognition is one of the most popular biometric modalities which has wide range of applications in biometric authentication. The most useful property of a Gabor filter is that it can achieve multi-resolution and multi-orientation analysis of an image. This paper presents an algorithm using Gabor wavelets in capturing discriminatory content, obtained by convolving a face image with coefficients of Gabor filter with different orientations and scales. Support vector machine (SVM) has been used to construct a robust classifier. This method has been tested with publicly available ORL dataset. This algorithm has been tested, cross-validated and the detailed results are presented. It is inferred that the proposed method offers a recognition rate (94 %) with tenfold cross-validation.

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References

  1. He ZS (2005) A SVM face recognition method based on Gabor-featured key points. In: 2005 international conference on machine learning and cybernetics, 2005

    Google Scholar 

  2. Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586–591, Maui, Hawaii, USA, 3–6 June 1991

    Google Scholar 

  3. Belhumeur PN, Hespanha JP, Kriegman DJ (1996) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: Proceedings of the 4th European conference on computer vision, ECCV’96, pp 45–58, Cambridge, UK, 15–18 April 1996

    Google Scholar 

  4. Nefian AV, Hayes MH III (1998) Hidden Markov models for face recognition. In: Proceedings of the IEEE international conference on acoustics, speech, and signal processing, ICASSP’98, vol 5, pp 2721–2724, Seattle, Washington, USA, 12–15 May 1998

    Google Scholar 

  5. Wiskott L, Fellous J-M, Krueuger N, von der Malsburg C (1999) Face recognition by elastic bunch graph matching, Chapter 11. In: Jain LC et al (eds) Intelligent biometric techniques in fingerprint and face recognition, CRC Press, pp 355–396, 1999

    Google Scholar 

  6. Liu C, Wechsler H (2000) Evolutionary pursuit and its application to face recognition. IEEE Trans Pattern Anal Mach Intell 22(6):570–582

    Article  Google Scholar 

  7. Lades M, Vorbruggen J, Buhmann J, Lange J, von der Malsburg C, Wurtz R, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42:300–311

    Article  Google Scholar 

  8. Yang P, Shan S, Gao W, Li S, Zhang D (2004) Face recognition using Ada-boosted Gabor features. In: Proceedings of IEEE international conference on automatic face and gesture recognition, pp 356–361, 2004

    Google Scholar 

  9. Gokberk B, Irfanoglu M, Akarun L, Alpaydm E (2003) Optimal Gabor kernel location selection for face recognition. In: Proceedings of international conference on image processing, vol 1, pp 677–680, 2003

    Google Scholar 

  10. Osuna E, Freund R, Girosit F (1997) Training support vector machines: an application to face detection. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, pp 130–136, 1997

    Google Scholar 

  11. Ramanathan R, Nair AS, Sagar VV, Sriram N, Soman KP (2009) A support vector machines approach for efficient facial expression recognition. In: International conference on advances in recent technologies in communication and computing, 2009. ARTCom ‘09

    Google Scholar 

  12. Moghaddam B, Yang MH (2000) Gender classification with support vector machines. In: Proceedings of the fourth IEEE international conference on automatic face and gesture recognition, pp 306–311, 2000

    Google Scholar 

  13. Arróspide J, Salgado L (2013) Log Gabor filters for image based vehicle verification. IEEE Trans Image Process 22(6), June 2013

    Google Scholar 

  14. Grigorescu SE, Petkov N, Kruizinga P (2002) Comparison of texture features based on Gabor filters. IEEE Trans Image Process 11(10), Oct 2002

    Google Scholar 

  15. Bhuiyan AA, Liu CH (2007) On face recognition using Gabor filters. Int J Comput Inf Syst Control Eng 1(4), 2007

    Google Scholar 

  16. The ORL face database is available at http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  17. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Networks 13(6):1450–1464

    Article  Google Scholar 

  18. Daugman J (1988) Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Trans Pattern Anal Mach Intell 36:1169–1179

    MATH  Google Scholar 

  19. Zhao GY, Pietik¨ainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928

    Article  Google Scholar 

  20. Soman KP, Loganathan R, Ajay V Machine Learning with SVM and other Kernel methods, Prentice Hall of India

    Google Scholar 

  21. Kong A (2008) An evaluation of Gabor orientation as a feature for face recognition. In: 19th international conference on pattern recognition, 2008

    Google Scholar 

  22. Sharif M, Khalid A, Raza M, Mohsin S (2011) Face recognition using Gabor filters. J Appl Comput Sci Math 11

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    R. Karthika

  2. Department of Computer Science Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Latha Parameswaran

Authors
  1. R. Karthika
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  2. Latha Parameswaran
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Correspondence to R. Karthika .

Editor information

Editors and Affiliations

  1. Noorul Islam Centre for Higher Education, Kumaracoil, Tamil Nadu, India

    L. Padma Suresh

  2. IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Karthika, R., Parameswaran, L. (2016). Study of Gabor Wavelet for Face Recognition Invariant to Pose and Orientation. In: Suresh, L., Panigrahi, B. (eds) Proceedings of the International Conference on Soft Computing Systems. Advances in Intelligent Systems and Computing, vol 397. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2671-0_48

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  • DOI: https://doi.org/10.1007/978-81-322-2671-0_48

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2669-7

  • Online ISBN: 978-81-322-2671-0

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