Abstract
Digital facial portrait photographs make up a massive portion of photos in the web. A number of methods for evaluating the aesthetics of photographs have been proposed recently. However, there have been a little work in the research community to address the aesthetics of targeted image domain, such as portraits. This paper introduces a new compositional-based augmentation scheme for aesthetic evaluation of portraits by well-known deep convolutional neural network (DCNN) models. We present a set of feature augmentation methods that take into account compositional photographic rules to ensure that the aesthetic in portraits are not hindered by standard transformations used for DCNN models. On a portrait subset of the large-scale AVA dataset, the proposed approach demonstrated a reasonable improvement in classification performance over the baseline and vanilla deep learning approaches.
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Notes
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This size was roughly determined by observation. Most of the other images appear to have substantially larger faces.
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Kairanbay, M., See, J., Wong, LK. (2017). Aesthetic Evaluation of Facial Portraits Using Compositional Augmentation for Deep CNNs. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_34
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