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Development of a naive bayes classifier for image quality assessment in biometric face images

Published:29 October 2019Publication History

ABSTRACT

Currently, the use of applications that requiring biometric facial authentication is increasing. However, the performance of these systems is compromised when the images do not present the quality required for their operation. Considering this, in the present work, a Naive Bayes classifier was developed for image quality assessment (IQA) dedicated to facial biometry systems. The metrics that characterize the image according to blur, sharpness and focus are used as input variables. A bibliographical study is carried out on the subject, deepening in the characteristics of the Bayesian classifiers. A public database is used to create the statistical data with which the Naive Bayes classifier is developed. Finally, the results of the classifier are presented for a set of conditions of the predictor variables.

References

  1. C. D. M. Regis, J. V. M. Cardoso, and M. S. Alencar, "Effect of Visual Attention Areas on the Objective Video Quality Assessment," in Anais do XVIII Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), 2012, pp. 75--78.Google ScholarGoogle Scholar
  2. T. Bubolz et al., "Video Quality Assessment of Early SKIP/DIS for 3D-HEVC Complexity Reduction," An. do XXIII Simpósio Bras. Sist. Multimídia e Web, pp. 73--79, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. N. Ponomarenko et al., "TID2008 - A database for evaluation of full-reference visual quality assessment metrics," Adv. Mod. Radioelectron., vol. 10, no. 4, pp. 30--45, 2009.Google ScholarGoogle Scholar
  4. J. R. Beveridge, D. S. Bolme, B. A. Draper, G. H. Givens, Y. M. Lui, and P. J. Phillips, "Quantifying How Lighting and Focus Affect Face Recognition Performance *," in IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. - Work. CVPRW 2010, 2010, pp. 74--81.Google ScholarGoogle Scholar
  5. C. Herrmann, C. Qu, D. Willersinn, and J. Beyerer, "Impact of resolution and image quality on video face analysis," in 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2015, pp. 1--6.Google ScholarGoogle Scholar
  6. X. Liu, M. Pedersen, C. Charrier, and P. Bours, "Performance evaluation of no-reference image quality metrics for face biometric images," J. Electron. Imaging, vol. 27, no. 02, pp. 1--24, Mar. 2018.Google ScholarGoogle ScholarCross RefCross Ref
  7. A. Abaza, M. A. Harrison, T. Bourlai, and A. Ross, "Design and evaluation of photometric image quality measures for effective face recognition," IET Biometrics, vol. 3, no. 4, pp. 314--324, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Patil and S. Sheelvant, "Survey on Image Quality Assessment Techniques," Int. J. Sci. Res., vol. 4, no. 7, pp. 1756--1759, 2015.Google ScholarGoogle Scholar
  9. J. V. de M. Cardoso, C. D. M. Regis, and M. S. de Alencar, "ImQET - Objective Stereoscopic Image Quality Evaluation Tool," in Anais do XX Simpósio Brasileiro de Sistemas Multimídia e WebB (WEBMEDIA), 2014, pp. 25--30.Google ScholarGoogle Scholar
  10. D. Z. Rodríguez, J. Abrahão, D. C. Begazo, R. L. Rosa, and G. Bressan, "Video quality subjective assessment considering cognitive criteria and user preferences on video content," in Anais do XVIII Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), 2012, pp. 269--272.Google ScholarGoogle Scholar
  11. K. H. Thung and P. Raveendran, "A survey of image quality measures," in International Conference for Technical Postgraduates 2009, TECHPOS 2009, 2009, pp. 1--4.Google ScholarGoogle Scholar
  12. V. K. Bhola, T. Sharma, and J. Bhatnagar, "Image Quality Assessment Techniques," Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. May, no. Special, pp. 156--161, 2014.Google ScholarGoogle Scholar
  13. S. B. Patil and S. R. Patil, "Survey on approaches used for image quality assessment," in 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017, pp. 987--991.Google ScholarGoogle Scholar
  14. S. Ahn, J. Park, and J. Chong, "Blurring image quality assessment method based on histogram of gradient," in Anais do XIX Simpósio Brasileiro de Sistemas Multimídia e Web, 2013, pp. 181--184.Google ScholarGoogle Scholar
  15. H. Yogita and H. Y. Patil, "A Survey on Image Quality Assessment Techniques, Challenges and Databases," Int. J. Comput. Appl., pp. 34--38, 2015.Google ScholarGoogle Scholar
  16. Ayman Abaza, Mary Ann Harrison, and Thirimachos Bourlai, "Quality metrics for practical face recognition," in 21st International Conference on Pattern Recognition (ICPR2012), 2012, pp. 3103--3107.Google ScholarGoogle Scholar
  17. A. M. Mansour, "Texture Classification using Naïve Bayes Classifier," IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 18, no. 1, pp. 112--121, 2018.Google ScholarGoogle Scholar
  18. H. Kaur and M. Kaur, "A Hybrid Approach for Blur Detection Using Naïve Bayes Nearest Neighbor Classifier," Inf. Technol. Comput. Sci., vol. 12, pp. 75--82, 2016.Google ScholarGoogle Scholar
  19. A. Khanin, M. Anton, M. Reginatto, and C. Elster, "Assessment of CT Image Quality Using a Bayesian Framework," IEEE Trans. Med. Imaging, vol. 37, no. 12, pp. 2687--2694, Dec. 2018.Google ScholarGoogle ScholarCross RefCross Ref
  20. J. Chaparro, B. G. Giraldo, and S. Rondón, "Evaluación del clasificador Naïve Bayes como herramienta de diagnóstico en Unidades de Cuidado Intensivo," Rev. Tecnol., vol. 12, no. 2, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  21. S. D. P. Leal, L. Gerardo, and D. Ortiz, "El clasificador Naïve Bayes en la extracción de conocimiento de bases de datos," Ingenierias, vol. VIII, no. 27, pp. 24--33, 2005.Google ScholarGoogle Scholar
  22. F. Crété-Roffet, T. Dolmiere, P. Ladret, M. Nicolas, and F. Crete, "The Blur Effect: Perception and Estimation with a New No-Reference Perceptual Blur Metric," in SPIE Electronic Imaging Symposium Conf Human Vision and Electronic Imaging, 2007, p. EI 6492-16.Google ScholarGoogle Scholar

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        cover image ACM Other conferences
        WebMedia '19: Proceedings of the 25th Brazillian Symposium on Multimedia and the Web
        October 2019
        537 pages
        ISBN:9781450367639
        DOI:10.1145/3323503

        Copyright © 2019 ACM

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        Publication History

        • Published: 29 October 2019

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