Low Level Features for Quality Assessment of Facial Images

Arnaud Lienhard, Patricia Ladret, Alice Caplier

Abstract

An automated system that provides feedback about aesthetic quality of facial pictures could be of great interest for editing or selecting photos. Although image aesthetic quality assessment is a challenging task that requires understanding of subjective notions, the proposed work shows that facial image quality can be estimated by using low-level features only. This paper provides a method that can predict aesthetic quality scores of facial images. 15 features that depict technical aspects of images such as contrast, sharpness or colorfulness are computed on different image regions (face, eyes, mouth) and a machine learning algorithm is used to perform classification and scoring. Relevant features and facial image areas are selected by a feature ranking technique, increasing both classification and regression performance. Results are compared with recent works, and it is shown that by using the proposed low-level feature set, the best state of the art results are obtained.

References

  1. Aydin, T., Smolic, A., and Gross, M. (2014). Automated Aesthetic Analysis of Photographic Images. IEEE Transactions on Visualization and Computer Graphics.
  2. Chang, C. and Lin, C. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technologies, pages 1-39.
  3. Crete, F., Dolmiere, T., Ladret, P., and Nicolas, M. (2007). The blur effect: perception and estimation with a new no-reference perceptual blur metric. In SPIE Electronic Image Symposium.
  4. Datta, R., Joshi, D., Li, J., and Wang, J. (2006). Studying aesthetics in photographic images using a computational approach. Computer VisionECCV 2006.
  5. Desnoyer, M. and Wettergreen, D. (2010). Aesthetic Image Classification for Autonomous Agents. 20th International Conference on Pattern Recognition, pages 3452-3455.
  6. Dhar, S., Ordonez, V., and Berg, T. (2011). High level describable attributes for predicting aesthetics and interestingness. Computer Vision and Pattern Recognition, pages 1657-1664.
  7. Faria, J., Bagley, S., R üger, S., and Breckon, T. (2013). Challenges of finding aesthetically pleasing images. In Image Analysis for Multimedia Interactive Services (WIAMIS), volume 2, pages 4-7.
  8. Hasler, D. and Suesstrunk, S. (2003). Measuring colorfulness in natural images. Electronic Imaging. International Society for Optics and Photonics., pages 87-95.
  9. He, K., Sun, J., and Tang, X. (2010). Single Image Haze Removal Using Dark Channel Prior. IEEE transactions on pattern analysis and machine intelligence.
  10. Jiang, W., Loui, A. C., and Cerosaletti, C. D. (2010). Automatic aesthetic value assessment in photographic images. IEEE International Conference on Multimedia and Expo, pages 920-925.
  11. Ke, Y., Tang, X., and Jing, F. (2006). The design of highlevel features for photo quality assessment. In Computer Vision and Pattern Recognition, volume 1, pages 419-426.
  12. Khan, S. and Vogel, D. (2012). Evaluating visual aesthetics in photographic portraiture. Computational Aesthetics in Graphics, Visualization and Imaging, pages 1-8.
  13. Li, C., Loui, A., and Chen, T. (2010). Towards aesthetics: a photo quality assessment and photo selection system. In Proceedings of the international conference on Multimedia, pages 10-13.
  14. Lienhard, A., Reinhard, M., Caplier, A., and Ladret, P. (2014). Photo Rating of Facial Pictures based on Image Segmentation. In Proceedings of the 9th Int. Conf. on computer Vision Theory and Applications, pages 329-336, Lisbonne, Portugal.
  15. Luo, Y. and Tang, X. (2008). Photo and video quality evaluation: Focusing on the subject. Computer VisionECCV 2008, pages 386-399.
  16. Males, M., Hedi, A., and Grgic, M. (2013). Aesthetic quality assessment of headshots. In 55th International Symposium ELMAR, number September, pages 25- 27.
  17. Marchesotti, L. and Perronnin, F. (2012). Óvaluation automatique de la qualité esthétique des photographies à l'aide de descripteurs d'images génériques. In Reconnaissance des Formes et Intelligence Artificielle (RFIA).
  18. Murray, N., Marchesotti, L., and Perronnin, F. (2012). AVA: A large-scale database for aesthetic visual analysis. Computer Vision and Pattern Recognition, pages 2408-2415.
  19. Pogac?nik, D., Ravnik, R., Bovcon, N., and Solina, F. (2012). Evaluating photo aesthetics using machine learning. In Data Mining and Data Warehouses, pages 4-7.
  20. Robnik- S?ikonja, M. and Kononenko, I. (2003). Theoretical and empirical analysis of ReliefF and RReliefF. Machine learning, 53:23-69.
  21. Tang, X., Luo, W., and Wang, X. (2013). Content-Based Photo Quality Assessment. IEEE Transactions on Multimedia, 15(8):1930-1943.
  22. Tong, Y., Konik, H., Cheikh, F. A., and Tremeau, A. (2010). Full reference image quality assessment based on saliency map analysis. Journal of Imaging Science and Technology, 54(3):1-21.
  23. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., volume 1, pages I-511-I-518. IEEE Comput. Soc.
  24. Willis, J. and Todorov, A. (2006). Making Up Your Mind After a 100-Ms Exposure to a Face. Psychological Science, 17(7):592-598.
  25. Wong, L.-k. and Low, K.-l. (2009). Saliency-enhanced image aesthetics class prediction. In 16th IEEE International Conference on Image Processing, pages 997- 1000. Ieee.
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Paper Citation


in Harvard Style

Lienhard A., Ladret P. and Caplier A. (2015). Low Level Features for Quality Assessment of Facial Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 545-552. DOI: 10.5220/0005308805450552


in Bibtex Style

@conference{visapp15,
author={Arnaud Lienhard and Patricia Ladret and Alice Caplier},
title={Low Level Features for Quality Assessment of Facial Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={545-552},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005308805450552},
isbn={978-989-758-089-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Low Level Features for Quality Assessment of Facial Images
SN - 978-989-758-089-5
AU - Lienhard A.
AU - Ladret P.
AU - Caplier A.
PY - 2015
SP - 545
EP - 552
DO - 10.5220/0005308805450552