Authors:
Arnaud Lienhard
;
Patricia Ladret
and
Alice Caplier
Affiliation:
Grenoble Images Parole Signal Automatique, France
Keyword(s):
Aesthetic Quality, Automatic Scoring, Portraits.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Visual Attention and Image Saliency
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.