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Facial Expression Recognition Using Entire Gabor Filter Matching Score Level Fusion Approach Based on Subspace Methods

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

Abstract

In this study appearance based facial expression recognition is presented by extracting the Gabor magnitude feature vectors (GMFV) and Gabor Phase Congruency vectors (GPCV). Feature vector space of these two vectors dimensions are reduced and redundant information is removed using subspace methods. Both GMFV and GPCV spaces are projected with Eigen score and projected matching scores are normalized and fused. Final matching score of each subspace method are normalized using Z-score normalization and fused together using maximum rule. Dimension of entire Gabor feature vector space consumes larger area of memory and high processing time with more redundant data. To overcome this problem in this paper entire Gabor matching score level fusion (EGMSLF) approach based on subspace methods is introduced. The JAFFE database is used for experiment. Support vector machine classifier technique is used as classifier. Performance evaluation is carried out by comparing proposed approach with state of art approaches. Proposed EGMSLF approach enhances the performance of earlier methods.

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References

  1. Bettadapura, V.: Face expression recognition and analysis: the state of the art. Proc. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1424–1445 (2002)

    Google Scholar 

  2. Ekman, P., Friesen, W.: Unmasking the Face A Guide to Recognizing Emotions from Facial Clues. Malor Books, Cambridge (2003)

    Google Scholar 

  3. Chang, Y.-K., Lien, C.-C., LinA, L.-Y.: New appearance -based facial expression recognition system with expression transaction matrices international. J. Innovative Comput. Inf. Control ICIC Int. 2009 ISSN 5(11)(B), 1349–4198, November 2009

    Google Scholar 

  4. Wang, S., Liu, Z., Wang, Z., Wu, G.: Analyses of a multimodal spontaneous facial expression database. IEEE Trans. Affect. Comput. 4(1), 34–46 (2012)

    Article  MATH  Google Scholar 

  5. Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. IEEE Pattern Recogn. 36(1), 259–275 (2003)

    Article  MATH  Google Scholar 

  6. St.George, B., Chang, C.C., Lin, C.J.: Facial expression recognition with SMS alert. In: IEEE International Conference on Optical Imaging Sensor and Security (ICOSS), July 2013

    Google Scholar 

  7. Hsieh, C., Lai, S., Chen, Y.: An optical flow based approach to robust face recognition under expression variations. IEEE Trans. Image Proc. 19(1), 233–240 (2010)

    Article  MathSciNet  Google Scholar 

  8. Valenti, R., Sebe, N., Gevers, T., Facial expression recognition: a fully integrated approach. In: 14th International Conference on Image Analysis and Processing Workshops, 2007. ICIAPW 2007, pp. 125–130. IEEE Conference Publications

    Google Scholar 

  9. Stathopoulou, I.-O., Tsihrintzis, G.A.: Comparative performance evaluation of artificial neural network-based vs. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds.) New Direct. in Intel. Interac. Multimedia. SCI, vol. 142, pp. 55–65. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. J. Inf. Process. Syst. 5(2), 69–78 (2009)

    Article  Google Scholar 

  11. Suruliandi, A., Rose, R.R., Meena, K.: A combined approach using textural and geometrical features for face recognition. ICTACT J. Image Video Process. 3(4), (2013). ISSN: 0976–9102

    Google Scholar 

  12. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)

    Article  Google Scholar 

  13. Samad, R., Sawada, H.: Extraction of the minimum number of Gabor wavelet parameters for the recognition of natural facial expressions. Artif. Life Robot. 16(1), 21–31 (2011). Springer

    Article  Google Scholar 

  14. Sisodia, P., Verma, A., Kansal, S.: Human facial expression recognition using gabor filter bank with minimum number of feature vectors. Int. J. Appl. Inf. Syst. 5(9), 9–13 (2013)

    Google Scholar 

  15. Thai, L.H., Nguyen Do Thai Nguyen, Hai, T.S.: A facial expression classification system integrating canny, principal component analysis and artificial neural network. arXiv preprint arXiv:1111.4052 (2011)

  16. Abdulrahman, M., Gwadabe, T.R., Abdu, F.J., Eleyan, A.: Gabor wavelet transform based facial expression recognition using PCA and LBP. In: Signal Processing and Communications Applications Confer and Communications Applications 2014 22nd, pp. 2265–2268. IEEE (2014)

    Google Scholar 

  17. Goyani, M., Dhorajiya, A., Paun, R.: Performance analysis of FDA based face recognition using correlation, ANN and SVM. Int. J. Artif. Intell. Neural Netw. 108–111 (2011)

    Google Scholar 

  18. Vitomir Struc and Nikola Pavesic, research Article, The Complete Gabor–Fisher Classifier for Robust Face Recogntion, EURASIP Journals on Advances in Signal Processing (2010)

    Google Scholar 

  19. Venkatesh, S., Owens, R.: An energy feature detection scheme. In: Proceedings of the Conference on Image Processing, pp. 553–557. Singapore (1989)

    Google Scholar 

  20. Kovesi, P.: Image features from phase congruency. Videre: J. Comput. Vis. Res. 1(3), 1–26 (1999)

    Google Scholar 

  21. Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Int. J. Pattern Recogn. 38, 2270–2285 (2005). ELSEVIER Publishers

    Article  Google Scholar 

  22. Muda, A.K., Huoy, C.Y., Hidayat, E., FajrianNur, A., Ahmad, S.: A Comparative study of feature extraction using PCA and LDA for face recognition. In: 7th International Conference on Information Assurance and Security, pp. 354–359, December 2011

    Google Scholar 

  23. Wang, C., Lan, L., Gu, M., Zhang, Y.: Face recognition based on principle component analysis and support vector machine. In: 3rd International Workshop on Intelligent Systems and Applications, pp. 1–4. May 2011

    Google Scholar 

  24. Lu, C., Jumahong, H., Liu, W.: A new rearrange modular two-dimensional LDA for face recognition. In: International Conference on Machine Learning and Cybernetics, vol. 1, pp. 361–366, July 2011

    Google Scholar 

  25. Ji, G.-L, Wan, J.-W., Yang, M.: Random sampling LDA incorporating feature selection for face recognition. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 180–185, July 2010

    Google Scholar 

  26. Shah, J.H., Sharif, M., Raza, M., Azeem, A.: A survey: linear and nonlinear pca based face recognition techniques. In: The International Arab Journal of Information Technology, vol. 10, no. 6, November 2013

    Google Scholar 

  27. Poh, N., Bourlai, N., Kittler, J.: Benchmarking quality-dependent and cost- sensitive score-level multimodal biometric fusion algorithms. IEEE. TIFS 4, 849–866 (2009)

    Google Scholar 

  28. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)

    Article  Google Scholar 

  29. Jin, Y., Ruan, Q.-Q.: Face recognition using Gabor-based improved supervised locality preserving projections computing and informatics 28, 81–95 (2009)

    Google Scholar 

  30. Praseeda L.V., Dr. Sasikumar, M.: Analysis of facial expression using gabor and SVM. Int. J. Recent Trends Eng. 1(2), 2009

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, SDMIT, Ujire, India

    Ganapatikrishna Hegde

  2. VTU, Belgaum, India

    Ganapatikrishna Hegde

  3. Department of Computer Science, GNITS, Hyderabad, India

    M. Seetha

  4. JNTU, Hyderabad, India

    M. Seetha

  5. Department of Computer Science, VCE, Hyderabad, India

    Nagaratna Hegde

Authors
  1. Ganapatikrishna Hegde
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  2. M. Seetha
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  3. Nagaratna Hegde
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Corresponding author

Correspondence to Ganapatikrishna Hegde .

Editor information

Editors and Affiliations

  1. Norwegian Univ. of Science & Technology, Trondheim, Norway

    Rajendra Prasath

  2. Intl Inst of Info Tech Hyderabad, Hyderabad, India

    Anil Kumar Vuppala

  3. V.H.N.S.N.College (Autonomous), Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Hegde, G., Seetha, M., Hegde, N. (2015). Facial Expression Recognition Using Entire Gabor Filter Matching Score Level Fusion Approach Based on Subspace Methods. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_6

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

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