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Evaluation of Quality Factors for the Captured Facial Image

by Abhay Goyal, Vikram Mutneja
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 142 - Number 10
Year of Publication: 2016
Authors: Abhay Goyal, Vikram Mutneja
10.5120/ijca2016909954

Abhay Goyal, Vikram Mutneja . Evaluation of Quality Factors for the Captured Facial Image. International Journal of Computer Applications. 142, 10 ( May 2016), 43-46. DOI=10.5120/ijca2016909954

@article{ 10.5120/ijca2016909954,
author = { Abhay Goyal, Vikram Mutneja },
title = { Evaluation of Quality Factors for the Captured Facial Image },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 142 },
number = { 10 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 43-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume142/number10/24936-2016909954/ },
doi = { 10.5120/ijca2016909954 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:44:40.158591+05:30
%A Abhay Goyal
%A Vikram Mutneja
%T Evaluation of Quality Factors for the Captured Facial Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 142
%N 10
%P 43-46
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In today's digital world, the scenario of the images has totally changed. We can do a lot of computation on the captured facial image for improving its quality. This is possible because of the availability of large number of digital platforms. These platforms have made the computational task easier and less time consuming. Instead of improving the quality of the captured image, we can firstly evaluate the quality of the image by making computation for the selected quality factors. After the evaluation, a proper decision can be made for the factors that need more improvement. Here we have selected sharpness, brightness, luminance, contrast and focus as quality factors which we consider more important for quality estimation and then these factors are calculated using the efficient method.

References
  1. Ayman Abaza, Mary Ann Harrison, Thirimachos Bourlai, "Quality Metrics for Practical Face Recognition", in 21st International Conference on Pattern Recognition (ICPR 2012), pp. 3103-3107,11-15 Nov. 2012.
  2. Kamal Nasrollahi, Thomas B. Moeslund, "Face Quality Assessment System in Video Sequences", Biometrics and Identity Management Lecture Notes in Computer Science, vol. 5372, pp. 10-18, 2008.
  3. Jiansheng Chen, Member, IEEE, Yu Deng, Gaocheng Bai and Guangda Su, "Face Image Quality Assessment Based on Learning to Rank", in IEEE Signal Processing Letters, vol. 22,No. 1, 2015, pp. 90-94.
  4. Ayman Abaza, Mary Ann Harrison, Thirimachos Bourlai, Arun Ross, "Design and evaluation of photometric image quality measures for effective face recognition", Biometrics, IET, vol. 3, Issue: 4, pp. 314-324.
  5. Teruaki Hirano, Yuki Nakagawa and Osamu Nakamura, "Highly Accurate Extraction of faces and facial parts taking into consideration people with glasses and the specific areas of the face for extracting specific features used in the recognition of facial expressions", Electrical and Computer Engineering, 23rd Canadian Conference, pp. 1-7, 2010.
  6. Mohammad A. Haque, Kamal Nasrollahi and Thomas B. Moeslund, "Constructing Facial Expression Log from Video Sequences using Face Quality Assessment", in Proceedings of the 9th International Conference on Computer Vision Theory and Applications, 2014, pp. 517-525.
  7. Debalina Bhattacharjee, Surya Prakash, and Phalguni Gupta, "No-Reference Image Quality Assessment for Facial Images", in Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence Lecture Notes in Computer Science, vol. 6839, pp. 594-601, 2012.
  8. Nguyen Thi Hai Binh, Nguyen Van Huan and Hakil Kim, "Combination of Edge and Color Information for Robust Preprocessing in Facial Image Quality Assessment", in Systems Man and Cybernetics (SMC) IEEE International Conference, pp. 3594-3600, 2010.
  9. Rein-Lien Vincent Hsu, Jidnya Shah, Brian Martin, "Quality Assessment of Facial Images", Biometric Consortium Conference, Biometrics Symposium: Special Session on Research, pp. 1-6, 2006.
  10. Krzysztof Kryszcsuk and Andrzej Drygajlo, "On Combining Evidence for Reliability Estimation in Face Verification", in 14th European Signal Processing Conference, pp. 1-5, 2006.
  11. P. Jonathon Phillips, J. Ross Beveridge, Bruse A. Draper, Alive J. O'Toole, Geof Givens ,et al, "An Introduction to the Good, the Bad, & the Ugly Face Recognition Challenge Problem", in IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), pp. 346-353, 2011.
  12. Jianfeng Long and Shutao Li, "Near Infrared Face Image Quality Assessment System of Video Sequences", in Sixty International Conference on Image and Graphics, pp. 275-279, 2011.
  13. Z. Wang and A. Bovik, "A universal image quality index", IEEE Signal Processing Letters, 9:81-84, 2002.
  14. T. Ahonen, A. Hadid, and M. Pietikainen, "Face description and local binary patterns: Application to face recognition", IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037-2041, Dec. 2006.
  15. Yuridia, O., Castillo, G., "Survey About Facial Image Quality", Fraunhofer Institute of Computer Graphics Research, 2006.
  16. Bezryadin, S., Bourov, P., Ilinih, D., "Brightness calculation in digital image processing", in International Symp. on Technologies for Digital Fulfillment, 2007.
  17. K. Kryszczuk and A. Drygajlo., "Impact of combining quality measures on biometric sample matching", in proc. of the IEEE BTAS, 2009.
  18. Yap, P.-T., Raveendran, P., "Image focus measure based on Chebyshev moments", in IEE Proc. Vis. Image Signal Process., pp. 128-136, 2004.
  19. A. Fourney and R. Laganiere, "Constructing Face Image Logs that are Both Complete and Concise", in 4th Canadian Conference on Computer Vision and Robot Vision, pp. 488-494, 2007.
  20. F. Weber, "Some quality measures for face images and their relationship to recognition performance", in Biometric Quality Workshop. National Institute of Standards and Technology, 2006.
  21. Briechle, K., and Hanebeck, "Template Matching using Fast Normalized Cross Correlation", in Proc. of SPIE Aero Sense Symposium, vol. 43-87, pp. 1-8, 2001.
  22. Marques, O., "Practical Image and Video Processing Using MATLAB", in Wiley-IEEE Press, 2011.
  23. Gao, X., Ki, S.Z., Liu, R., Zhang, "Standardization of face image sample quality", in Int. Conf. on Biometrics (ICB), 2007.
  24. Gao, X., Ki, S.Z., Liu, R., Zhang, "The CAS-PEAL large-scale Chinese face database and baseline evaluations", in IEEE Trans. Syst. Man Cybern., 38, pp. 149-161, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Quality factors and Facial image.

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