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A Fuzzy Approach to Recognize Face Using Contourlet Transform

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Abstract

Face recognition addresses identification, verification, and authentication in biometric-based security systems. This work enhances the contrast and edges in face images and recognizes the face using contourlet transform and fuzzy rules. Contourlet transformed image provides multiscale and directional information. The transformed image is divided into low-pass image (low-frequency image) and band-pass image (high-frequency image). The low-pass image is enhanced using fuzzy-based histogram specification since it deals with contrast. Band-pass image contains detailed information about the edges of the image and are enhanced using fuzzy rules and morphological gradient operators. The proposed system achieves the accuracy rate of 99.81% and 99.35% on Yale-B and JAFEE dataset, respectively, which is better than the existing curvelet and wavelet transform-based recognition. The incorporation of fuzzy rules enhances the mean intensity value of the edges to 34.19, which is better than Canny, Sobel, Prewitt, Robert and Laplacian edge detection techniques. Finally Discriminant Correlation Analysis (DCA) feature level fusion is applied to fuse enhanced edge intensities and histogram features for Support Vector Machine (SVM) classification.

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References

  1. Anwar, S.: A neural network approach to edge detection using adaptive neuro—fuzzy inference system, IEEE International conference on advances in computing, communications and informatics, pp. 2432–2435. (2014)

  2. Asadi, H., Kaboli, S., H., Mohammadi, A., Oladazimi, M.: Fuzzy logic control technique in li-ion battery charger. In: International conference on electrical, electronics and civil engineering. pp. 179–183. (2011)

  3. Asadi, H., Kaboli, S., Oladazimi, M., Safari, M.: A review on Li-ion battery charger techniques and optimize battery charger performance by fuzzy logic. Int. Conf. Inform. Intell. Comput. 18(201), 89–96 (2011)

    Google Scholar 

  4. Biswas, S.: An efficient face recognition method using contourlet transform. In: IEEE international conference on Research in computational intelligence and communication Networks, pp. 27–31 (2015)

  5. Chakraborty, Aruna, Konar, Amit: Emotion recognition from facial expressions and its control using fuzzy logic. IEEE Trans. Syst. Man Cybern. 39(4), 726–743 (2009)

    Article  Google Scholar 

  6. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)

    Article  Google Scholar 

  7. Chitaliya, N.G.: An efficient method for face feature extraction and recognition based on contourlet transforms and principal component analysis. Procedia Comput. Sci. 2, 52–61 (2010)

    Article  Google Scholar 

  8. Do, Vetterli: The contourlet transformation: an efficient directional multi resolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)

    Article  Google Scholar 

  9. Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Discriminant correlation analysis: real-time feature level fusion for multimodal biometric recognition. IEEE Trans. Inform. Forens. Secur. 11(9), 1984–1996 (2016)

    Article  Google Scholar 

  10. Lee, George, Madabhushi, Anant: Supervised multi-view canonical correlation analysis (SMVCCA): integrating histologic and proteomic features for predicting recurrent prostate cancer. IEEE Trans. Med. Image Process. 34(1), 284–297 (2015)

    Article  Google Scholar 

  11. Lee, Ping-Han, Szu-Wei, Wu, Hung, Yi-Ping: Illumination compensation using oriented local histogram equalization and its application to face recognition. IEEE Trans. Image Process. 21(9), 4280–4289 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu, J., Huang, J., Lin, W., Zhou, Y., Mu, G.: A novel face recognition approach based on curvelet transform. In: IEEE international conference on computer and communications, pp. 423–427. (2016)

  13. Mamoria, P., Raj, D.: An analysis of image using fuzzy contrast enhancement Techniques. In: IEEE 3rd international conference on computing for sustainable global development, pp. 288–291, (2016)

  14. Mehta, S., Gupta, S., Bhushan, B., Nagpal, C.K.: Face recognition using neuro-fuzzy inference system. Int. J. Signal Process. Image Process. Pattern Recogn. 7(1), 331–334 (2014)

    Google Scholar 

  15. Melin, P.: Edge-detection method for image processing based on generalized type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 22, 6 (2014)

    Article  Google Scholar 

  16. Nagaraja, S., Prabhakar, C., J., Kumar, P., P.: Complete local binary pattern for representation of facial expression based on Curvelet transform. In: International Conference on Multimedia Processing, Communication and Information Technology (MPCIT), pp. 48–56, (2013)

  17. Nirmala, D.,E,: Comparison of pixel-level and feature level image fusion methods. In: IEEE international conference on computing for sustainable global development, pp. 743–748. (2015)

  18. Pankaj, D.S., Wilscy, M.: Face recognition using fuzzy neural network classifier, pp. 53–62. Springer, CCIS (2011)

    Google Scholar 

  19. Patil, H., Y., Kothari, A., G.: Expression invariant face recognition using contourlet transform. In: IEEE 4th international conference on image processing theory, tools and applications, pp 1–5, (2014)

  20. Shan, Du: Adaptive region-based image enhancement method for robust face recognition under variable illumination conditions. IEEE Trans. Circ. Syst. Video Technol. 20(9), 1165–1175 (2010)

    Article  Google Scholar 

  21. Sharma, Sonal: Contrast enhancement of an image using fuzzy logic. Int. J. Comput. Appl. 111(17), 14–20 (2015)

    Google Scholar 

  22. Susan, S., Jain, A., Sharma, A., Verma, S., Jain, S.: Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision. IET Image Processing. 9(11), 951–958 (2015)

    Article  Google Scholar 

  23. Vapnik, V.N.: The nature of statistical learning theory, 2nd edn, pp. 138–141. Springer, New York (1999)

    Google Scholar 

  24. Xie, Xiaohua: Extraction of illumination invariant facial features from a single image using non subsampled contourlet transform. Patt. Recogn. 43, 4177–4189 (2010)

    Article  MATH  Google Scholar 

  25. Zhang, Y.D., Yang, Z.J., Lu, H.M., Zhou, X.X., Phillips, P., Liu, Q.M., Wang, S.H.: Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access. 22(4), 8375–8385 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, College of Engineering, Anna University, Chennai, Tamil Nadu, India

    K. Seethalakshmi & S. Valli

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  1. K. Seethalakshmi
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  2. S. Valli
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Correspondence to K. Seethalakshmi.

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Seethalakshmi, K., Valli, S. A Fuzzy Approach to Recognize Face Using Contourlet Transform. Int. J. Fuzzy Syst. 21, 2204–2211 (2019). https://doi.org/10.1007/s40815-019-00703-0

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  • DOI: https://doi.org/10.1007/s40815-019-00703-0

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