Low Level Features for Quality Assessment of Facial Images

Arnaud Lienhard, Patricia Ladret, Alice Caplier


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.


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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

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)},

in EndNote Style

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