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Volume: 2 | Article ID: art00003
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Portrait Quality Assessment using Multi-Scale CNN
  DOI :  10.2352/issn.2694-118X.2021.LIM-5  Published OnlineSeptember 2021
Abstract

In this paper, we propose a novel and standardized approach to the problem of camera-quality assessment on portrait scenes. Our goal is to evaluate the capacity of smartphone front cameras to preserve texture details on faces. We introduce a new portrait setup and an automated texture measurement. The setup includes two custom-built lifelike mannequin heads, shot in a controlled lab environment. The automated texture measurement includes a Region-of-interest (ROI) detection and a deep neural network. To this aim, we create a realistic mannequins database, which contains images from different cameras, shot in several lighting conditions. The ground-truth is based on a novel pairwise comparison technology where the scores are generated in terms of Just-Noticeable-differences (JND). In terms of methodology, we propose a Multi-Scale CNN architecture with random crop augmentation, to overcome overfitting and to get a low-level feature extraction. We validate our approach by comparing its performance with several baselines inspired by the Image Quality Assessment (IQA) literature.

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  Cite this article 

Chahine Nicolas, Belkarfa Salim, "Portrait Quality Assessment using Multi-Scale CNNin Proc. IS&T London Imaging Meeting 2021: Imaging for Deep Learning,  2021,  pp 5 - 10,  https://doi.org/10.2352/issn.2694-118X.2021.LIM-5

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