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Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques

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Advances in Information and Communication (FICC 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1364))

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Abstract

In this work, preprocessing methods are applied to both whole image and block sized image to improve the performance of face recognition. In holistic method, preprocessing methods like Locally Tuned Inverse Sine Nonlinear (LTISN), Self-Quotient Illumination (SQI), Gamma Intensity Correction (GIC), Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Difference of Gaussian (DoG) filtering are applied to the complete image (holistic) and then the images are block sized. Subsequently, K- Nearest Neighbor (K-NN) classifier and weighted entropy-based fusion method are applied. In the block-based method, input image is divided into small blocks in a non-overlapping pattern. Then above-mentioned preprocessing techniques are applied to each block. Subsequently K-NN classifier and weighted entropy-based fusion method are applied to the output of each classifier. The recognition accuracy is computed for various block sizes and threshold values. This paper presents comparison of various pre-processing methods applied to both the holistic and block-based methods to mitigate the effects of illumination variation and preserve the essential details that are needed for recognition. The applied preprocessing methods enhance the image quality and increases the face recognition accuracy on FERET dataset under lighting variations. The block-based weighted entropy-based fusion method performs better than holistic method in FERET database. The maximum accuracy of the proposed method increases from 85.1% to 88.2% after the application of preprocessing methods.

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References

  1. Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. J. Inf. Process. Syst. 5(2), 41–68 (2009)

    Article  Google Scholar 

  2. Admi, Y., Moses, Y., Ulman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 721–732 (1997)

    Article  Google Scholar 

  3. Selvi, S.M., Prabhu, P.: Digital image processing techniques – a survey. Golden Res. Thoughts J. 5(11), 1–7 (2016)

    Google Scholar 

  4. Nikan, S., Ahmadi, M.: Human face recognition under occlusion using LBP and entropy weighted voting. In: 21st International Conference on Pattern Recognition ICPR, pp. 1699–1702 (2012)

    Google Scholar 

  5. Arigela, S., Asari, V.K.: Self-tunable transformation function for enhancement of high contrast color images. J. Electron. Imaging 22(2), 023010 (2013)

    Article  Google Scholar 

  6. Wang, H., Li, S.Z., Wang, Y., Zhang, J.: Self-quotient image for face recognition. In: 2004 International Conference on Image Processing, 2004, ICIP 2004, Singapore, vol. 2, pp. 1397–1400 (2004)

    Google Scholar 

  7. Wang, H., Li, S.Z., Wang, Y.: Face recognition under varying lighting conditions using self quotient image. In: Proceedings of IEEE International Conference on Automatic FGR (AFGR), pp. 819–824 (2004)

    Google Scholar 

  8. Anila, S., Arulsukanya, K., Divya, K., Kanimozhi, C., Kavitha, D.: An efficient preprocessing technique for face recognition under difficult lighting conditions. In: Proceedings of National Conference on Emerging Trends in Computer Communication and Informatics (ETCCI-2011), March 10–11 2011

    Google Scholar 

  9. Karthikeyan, V., Vijayalakshmi, K., Jeyakumar, P.: An efficient method for face recognition in various assorted conditions. Biom. Bioinform. 4(12), 543–547 (2012)

    Google Scholar 

  10. Amiri, S.A., Hassanpour, H.: A preprocessing approach for image analysis using gamma correction. Int. J. Comput. Appl. 38(12), 38–46 (2012)

    Google Scholar 

  11. Koringa, P.A., Mitra, S.K., Asari, V.K.: Handling illumination variation: a challenge for face recognition. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds.) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol. 460. Springer, Singapore (2017)

    Google Scholar 

  12. Anila, S., Devarajan, N.: Preprocessing technique for face recognition applications under varying illumination conditions. Glob. J. Comput. Sci. Technol. 12(11-F), 13 (2012)

    Google Scholar 

  13. Biglari, M., Mirzaei, F., Ebrahimpour-Komeh, H.: Illumination invariant face recognition using SQI and weighted LBP histogram. In: 2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA), pp. 1–7. IEEE (2013)

    Google Scholar 

  14. Calvo, G., Baruque, B., Corchado, E.: Study of the pre-processing impact in a facial recognition system. In: Pan, J.S., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds.) Hybrid Artificial Intelligent Systems, HAIS. Lecture Notes in Computer Science, vol. 8073. Springer, Heidelberg (2013)

    Google Scholar 

  15. Phillips, P.J., Rizvi, S.A., Moon, H.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  16. Nabatchian, A., Abdel-Raheem, E., Ahmadi, M.: Illumination invariant feature extraction and mutual information-based local matching for face recognition under illumination variation and occlusion. Pattern Recognit. 41(10–11), 2576–2587 (2011)

    Article  Google Scholar 

  17. Dhriti, Kaur, M.: K-nearest neighbor classification approach for face and fingerprint at feature level fusion. Int. J. Comput. Appl. 60, 13–17 (2017)

    Google Scholar 

  18. Topc, B., Erdogan, H.: Decision fusion for patch-based face recognition. In: ICPR, pp. 1348–1351 (August 2010)

    Google Scholar 

  19. Roli, F., Kittler, J., Fumera, G., Muntoni, D.: An experimental comparison of classifier fusion rules for multimodal personal identity verification systems. In: MCS, pp. 325–335 (2002)

    Google Scholar 

  20. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. 38, 35–44 (2004)

    Article  Google Scholar 

  21. Krieger, E., Asari, V.K., Arigela, S.: Color image enhancement of low-resolution images captured in extreme lighting conditions. In: SPIE Sensing Technology and Applications, pp. 91200Q–91200Q. International Society for Optics and Photonics (2014)

    Google Scholar 

  22. Varadarajan, K., Suhasini, P., Manikantan, K., Ramachandran, S.: Face recognition using block based feature extraction with CZT and Goertzel-algorithm as a preprocessing technique. Procedia Comput. Sci. 46, 1458–1467 (2015)

    Article  Google Scholar 

  23. Han, H., Shan, S., Chen, X., Gao, W.: A comparative study on illumination preprocessing in face recognition. Pattern Recognit. 46(6), 1691–1699 (2013)

    Article  Google Scholar 

  24. Nishiyama, M., Kozakaya, T., Yamaguchi, O.: Illumination normalization using quotient image-based techniques. In: Recent Advances in Face Recognition, I-Tech, Vienna, Austria, pp. 98–108 (December 2008)

    Google Scholar 

  25. Vinothkumar, B., Kumar, P.: Gamma correction technique based feature extraction for face recognition system. Int. J. Comput. Intell. Inform 3(1), 20–26 (2013)

    Google Scholar 

  26. Zimmerman, J.B., Cousins, S.B., Hartzell, K.M., Frisse, M.E., Kahn, M.G.: A psychophysical comparison of two methods for adaptive histogram equalization. J. Digit. Imaging 2(2), 82–91 (1989)

    Article  Google Scholar 

  27. Saurav, S., Singh, S., Saini, R., Saini, A.K., Sharma, N.: A comparative analysis of various image enhancement techniques for facial images. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), India, pp. 2279–2284 (August 2015)

    Google Scholar 

  28. Min, B.S., Lim, D.K., Kim, S.J., Lee, J.H.: A novel method of determining parameters of CLAHE based on image entropy. Int. J. Softw. Eng. Appl. 7(5), 113–120 (2013)

    Google Scholar 

  29. Suganya, P., Gayathri, S., Mohanapriya, N.: Survey on image enhancement techniques. Int. J. Comput. Appl. Technol. Res. 2(5), 623–627 (2013)

    Google Scholar 

  30. Longkumer, N., Kumar, M., Saxena, R.: Contrast enhancement techniques using histogram equalization: a survey. Int. J. Curr. Eng. Technol. 4(3), 1561–1565 (2014). E-ISSN 2277-4106, P-ISSN 2347-5161

    Google Scholar 

  31. Sri Krishna, A., Srinivasa Rao, G., Sravya, M.: Contrast enhancement techniques using histogram equalization methods on color images with poor lightning. Int. J. Comput. Sci. Eng. Appl. (IJCççSEA) 3(4), 15 (2013)

    Google Scholar 

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Authors and Affiliations

  1. University of Windsor, 401 Sunset Ave., Windsor, ON, N9B 3P4, Canada

    Jayanthi Raghavan & Majid Ahmadi

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  1. Jayanthi Raghavan
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  2. Majid Ahmadi
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Correspondence to Majid Ahmadi .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Raghavan, J., Ahmadi, M. (2021). Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques. In: Arai, K. (eds) Advances in Information and Communication. FICC 2021. Advances in Intelligent Systems and Computing, vol 1364. Springer, Cham. https://doi.org/10.1007/978-3-030-73103-8_51

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