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 https://github.com/zhanglijun95/FIIQA.
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This work was supported in part by the Natural Science Foundation of China under grant no. 61672380 and in part by the ZTE Industry-Academia-Research Cooperation Funds under grant no. CON1608310007.
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Zhang, L., Zhang, L., Li, L. (2017). Illumination Quality Assessment for Face Images: A Benchmark and a Convolutional Neural Networks Based Model. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10636. Springer, Cham. https://doi.org/10.1007/978-3-319-70090-8_59
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DOI: https://doi.org/10.1007/978-3-319-70090-8_59
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