350 rub
Journal Achievements of Modern Radioelectronics №10 for 2016 г.
Article in number:
Face image quality metrics investigation for image recognition tasks
Keywords: face image quality assessment face recognition symmetry correlation coefficient
Authors:
I.S. Nenakhov - Post-graduate Student, P.G. Demidov Yaroslavl State University E-mail: zergoodsound@gmail.com V.V. Khryashchev - Ph.D. (Eng.), Associate Professor, P.G. Demidov Yaroslavl State University E-mail: vhr@yandex.ru А.L. Priorov - Dr.Sc. (Eng.), Associate Professor, P.G. Demidov Yaroslavl State University E-mail: andcat@yandex.ru А.А. Lebedev - Master Student, P.G. Demidov Yaroslavl State University E-mail: lebedevdes@gmail.com
Abstract:
Set of face image quality metrics: accuracy, resolution, symmetry, contrast, metric based ranking learning is studied. A new face quality metric based on the symmetry of the singular face points is proposed. For all metrics coefficients of Spearman rank correlation with subjective expert assessment at different levels of illumination of the scene are calculated. The results will allow to optimize biometric identification system using facial image.
Pages: 48-53
References

 

  1. Zhao W., Chellappa R., Phillips P., Rosenfeld A. Face recognition: A literature survey // ACM Computing Surveys (CSUR). 2003. V. 35. № 4. P. 399-458.
  2. Ozay N., Tong Y., Frederick W., Liu X. Improving face recognition with a quality-based probabilistic framework // In Computer Vision and Pattern Recognition (CVPR) Biometrics Workshop. 2009. R. 134-141.
  3. Zhu X., Ramanan D. Face detection, pose estimation and landmark localization in the wild // Proc. of the IEEE Conference on Computer Vision and Pattern Recognition. 2012. P. 2879-2886.
  4. Gao X., Li S. Z., Liu R., Zhang P. Standardization of face image sample quality // Proc. Int. Conf. Biometrics. 2007. P. 242-251.
  5. Nasrollahi K., Moeslund T.B. Face quality assessment system in video sequences // BIOID, Lecture Notes in Computer Science (LNCS). 2008. V. 5372. P. 10-18.
  6. Wong Y., Chen S., Mau S., Sanderson C., Lovell B.C. Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition // Computer Vision and Pattern Recognition (CVPR) Workshops. 2011. P. 74-81.
  7. Chen J., Deng Y., Bai G., Su G. Face Image quality assessment based on learning to rank // IEEE Signal Processing Letters. 2015. V. 22. № 1. P. 90-94.
  8. Chen J., Yang C., Deng Y., Zhang G., Su G. Exploring facial asymmetry using optical flow // IEEE Signal Processing Letters. 2014. V. 21. № 7. P. 792-795.
  9. Nikitin M., Konushin A., Konushin V. Face quality assessment for face verification in video // Proceedings of GraphiCon\'2014. 2014. P. 111-114.
  10. Hadid A., Pietikainen M. From still image to video-based face recognition: An experimental analysis // In Proc. Automatic Face and Gesture Recognition (AFGR). 2004. P. 813-818.
  11. Berrani S., Garcia C. Enhancing face recognition from video sequences using robust statistics // In IEEE International Conference on Video and Signal Based Surveillance (AVSS). 2005. P. 324-329.
  12. Nenakhov I.S., KHrjashhev V.V., Pavlov V.A. Ocenka kachestva izobrazhenija lica v zadachakh biometricheskojj identifikacii // Cifrovaja obrabotka signalov i ee primenenie (DSPA-2016): Dokl. 18-jj mezhdunar. konf. Moskva. 2016. T. 2. S. 954-959.
  13. Nikitin A.E., KHrjashhev V.V., Priorov A.L., Matveev D.V. Razrabotka i analiz algoritma raspoznavanija lic na osnove lokalnykh kvantovannykh shablonov // Nelinejjnyjj mir. 2014. № 8. S. 35-42.
  14. ISO/IEC 19794-5 (published version). Information technology - Biometric Data Interchange Formats. 2005.
  15. Machine readable travel documents. International Civil Aviation Organization. 2006.
  16. Sang J., Lei Z., Li S.Z. Face image quality evaluation for ISO/IEC standards 19794-5 and 29794-5 // In ICB, Lecture Notes in Computer Science (LNCS). 2009. V. 5558. P. 229-238.
  17. Yang Z., Ai H., Wu B., Lao S., Cai L. Face pose estimation and its application in video shot selection // In International Conference on Pattern Recognition (ICPR). 2004. P. 322-325.
  18. Zhang G., Wang Y. Asymmetry-based quality assessment of face images // In ISVC, Lecture Notes in Computer Science (LNCS). 2009. V. 5876. P. 499-508.
  19. Nenakhov I.S., Brjukhanov JU.A., KHrjashhev V.V. Neehtalonnaja ocenka kachestva izobrazhenijj na osnove mnogomasshtabnykh lokalnykh binarnykh shablonov // Radioehlektronnye ustrojjstva i sistemy dlja infokommunikacionnykh tekhnologijj ? REHUS-2016: Sb. dokl. mezhdunar. konf. Moskva. T. 2. S. 378-382."Uspekhi sovremennojj radioehlektroniki", 2016 g., № 10 53
  20. Feng Z., Huber P., Kittler J., Christmas W., Wu X.J. Random cascaded-regression copse for robust facial landmark detection // IEEE Signal Processing Letters. 2015. V. 22. № 1. P. 76-80.
  21. Howse J. OpenCV Computer vision with Python. Packt Publishing Ltd. 2013.