http://iet.metastore.ingenta.com
1887

Design and evaluation of photometric image quality measures for effective face recognition

Design and evaluation of photometric image quality measures for effective face recognition

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real-time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the author's studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.

References

    1. 1)
      • 1. Jain, A., Ross, A., Nandakumar, K.: ‘Introduction to Biometric’ (Springer-Verlag New York, Inc., 2011).
    2. 2)
    3. 3)
      • 3. Merkle, J., Schwaiger, M., Breitenstein, M.: ‘Towards improving the NIST fingerprint image quality (NFIQ) algorithm’. Int. Conf. Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 2010.
    4. 4)
      • 4. Hsu, R.L.V., Shah, J., Martin, B.: ‘Quality assessment of facial images’. Biometric Consortium Conf. (BCC), Baltimore, MD, USA, 2006.
    5. 5)
      • 5. Bhattacharjee, D., Prakash, S., Gupta, P.: ‘No-Reference image quality assessment for facial images’. Seventh Int. Conf. on Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence, Zhengzhou, China, 2011.
    6. 6)
      • 6. Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.: ‘Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), Colorado Springs, CO, USA, 2011, pp. 7481.
    7. 7)
      • 7. Kryszczuk, K., Drygajlo, A.: ‘On combining evidence for reliability estimation in face verification’. European Signal Processing Conf. (EUSIPCO), Florence, Italy, 2006.
    8. 8)
    9. 9)
      • 9. Adler, A., Dembinsky, T.: ‘Human vs. automatic measurement of biometric sample quality’. IEEE Canadian Conf. on Electrical and Computer Engineering (CCECE), Ottawa, Canada, 2006.
    10. 10)
      • 10. Vatsa, M., Singh, R., Noore, A.: ‘SVM-based adaptive biometric image enhancement using quality assessment’, in Prasad, B., Prasanna, S. (Eds.): ‘Speech, Audio, Image and Biomedical Signal Processing using Neural Networks, ser. Studies in Computational Intelligence’ (Springer Berlin Heidelberg, 2008), vol. 83, pp. 351367.
    11. 11)
      • 11. Sang, J., Lei, Z., Li, S.Z.: ‘Face image quality evaluation for ISO/IEC standards 19794–5 and 29794–5’. Int. Conf. on Biometrics (ICB), Sassari, Italy, 2009.
    12. 12)
      • 12. Gao, X., Li, S.Z., Liu, R., Zhang, P.: ‘Standardization of face image sample quality’. Int. Conf. on Biometrics (ICB), Seoul, Korea, 2007.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 17. Phillips, P.J., Flynn, P.J., Beveridge, J.R., et al: ‘Overview of the multiple biometrics grand challenge’. Third Int. Conf. on Biometrics (ICB), Alghero, Italy, 2009.
    18. 18)
      • 18. Phillips, P.J., Beveridge, J.R., Draper, B.A., et al: ‘An introduction to the good, the bad, and the ugly face recognition challenge problem’. IEEE Int. Conf. on Automatic Face and Gesture Recognition and Workshops (FG), Santa Barbara, CA, USA, 2011.
    19. 19)
      • 19. Johnson, P., Lopez-Meyer, P., Sazonova, N., Hua, F., Schuckers, S.: ‘Quality in face and iris research ensemble QFIRE’. IEEE Int. Conf. on Biometrics: Theory Applications and Systems (BTAS), Washington, DC, USA, 2010.
    20. 20)
    21. 21)
      • 21. Nasrollahi, K., Moeslund, T.B.: ‘Face quality assessment system in video sequences’. European Workshop on Biometrics and Identity Management (BIOID), Roskilde, Denmark, 2008.
    22. 22)
    23. 23)
      • 23. Poh, N., Kittler, J., Rattani, A., Tistarelli, M.: ‘Group-specific score normalization for biometric systems’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR) Workshops, San Francisco, CA, USA, 2010.
    24. 24)
    25. 25)
      • 25. Bhatt, H.S., Bharadwaj, S., Vatsa, M., Singh, R., Ross, A., Noore, A.: ‘A framework for quality-based biometric classifier selection’. IEEE Int. Joint Conf. on Biometrics (IJCB), Washington, DC, USA, 2010.
    26. 26)
      • 26. Kryszczuk, K., Drygajlo, A.: ‘Improving classification with class-independent quality measures: Q-stack in face verification’. Int. Conf. on Biometrics (ICB), Seoul, Korea, 2007.
    27. 27)
    28. 28)
      • 28. Wyszecki, G., Stiles, W.S.: ‘Color science. Concepts and methods, quantitative data and formulae’ (John Wiley & Sons, 2000).
    29. 29)
      • 29. Bezryadin, S., Bourov, P., Ilinih, D.: ‘Brightness calculation in digital image processing’. Int. Symp. on Technologies for Digital Fulfillment, Las Vegas, NV, USA, 2007.
    30. 30)
    31. 31)
    32. 32)
      • 32. Kryszczuk, K., Richiardi, J., Drygajlo, A.: ‘Impact of combining quality measures on biometric sample matching’. IEEE Int. Conf. on Biometrics: Theory Applications and Systems (BTAS), Washington, DC, USA, 2009.
    33. 33)
      • 33. Kalka, N.D., Zuo, J., Schmid, N.A., Cukic, B.: ‘Image quality assessment for iris biometric’. SPIE Conf. on Defense, Security, and Sensing: Biometric Technology for Human Identification III, Orlando, FL, USA, 2006.
    34. 34)
      • 34. Abaza, A., Harrison, M.A., Bourlai, T.: ‘Quality metrics for practical face recognition’. 21th Int. Conf. on Pattern Recognition (ICPR), Tsukuba, Japan, 2012.
    35. 35)
      • 35. Klare, B., Jain, A.: ‘On a taxonomy of facial features’. IEEE Int. Conf. on Biometrics: Theory Applications and Systems (BTAS), Washington, DC, USA, 2010.
    36. 36)
    37. 37)
      • 37. Zhao, Y.: ‘Theories and applications of LBP: a survey’. Seventh Int. Conf. on Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence, Zhengzhou, China, 2011.
    38. 38)
      • 38. Tan, X., Triggs, B.: ‘Enhanced local texture feature sets for face recognition under difficult lighting conditions’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 19, pp. 16351650.
    39. 39)
    40. 40)
    41. 41)
    42. 42)
    43. 43)
    44. 44)
      • 44. Bourlai, T., Kittler, J., Messer, K.: ‘JPEG compression effects on a smart card face verification system’. Machine Vision Applications, Tokyo, Japan, 2005.
    45. 45)
      • 45. Bourlai, T., Messer, K., Kittler, J.: ‘Face verification system architecture using smart cards’. Pattern Recognition, Cambridge, UK, 2004.
    46. 46)
      • 46. Bourlai, T., Kalka, N.D., Cao, D., et al: ‘Ascertaining human identity in night environments’, in Bhanu, B., Ravishankar, C.V., Roy-Chowdhury, A.K., Aghajan, H., Terzopoulos, D. (Eds.): ‘Distributed Video Sensor Networks’ (Springer: London, 2011), vol. 133, pp. 451467.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2014.0022
Loading

Related content

content/journals/10.1049/iet-bmt.2014.0022
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address