Low Level Features for Quality Assessment of Facial Images

Arnaud Lienhard 1 Patricia Ladret 1 Alice Caplier 1
1 GIPSA-AGPIG - AGPIG
GIPSA-DIS - Département Images et Signal
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.
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Arnaud Lienhard, Patricia Ladret, Alice Caplier. Low Level Features for Quality Assessment of Facial Images. 10th International Conference on Computer Vision Theory and Applications (VISAPP 2015), Mar 2015, Berlin, Germany. pp.545-552. ⟨hal-01133674⟩

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