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Title: Illumination quality assessment for face images : a benchmark and a convolutional neural networks based model
Authors: Zhang, L
Zhang, L
Li, L 
Issue Date: 2017
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10636 LNCS, p. 583-593
Abstract: Many institutions, such as banks, usually require their customers to provide face images under proper illumination conditions. For some remote systems, a method that can automatically and objectively evaluate the illumination quality of a face image in a human-like manner is highly desired. However, few studies have been conducted in this area. To fill this research gap to some extent, we make two contributions in this paper. Firstly, in order to facilitate the study of illumination quality prediction for face images, a large-scale database, namely, Face Image Illumination Quality Database (FIIQD), is established. FIIQD contains 224,733 face images with various illumination patterns and for each image there is an associated illumination quality score. Secondly, based on deep convolutional neural networks (DCNN), a novel highly accurate model for predicting the illumination quality of face images is proposed. To make our results reproducible, the database and the source codes have been made publicly available at
Keywords: Convolutional neural networks
Illumination quality assessment
Illumination transfer
Publisher: Springer Verlag
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 9783319700892
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-70090-8_59
Description: 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14-18 November, 2017
Appears in Collections:Conference Paper

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