Abstract
Do women look at aesthetics differently from men? Does cultural background have an influence over the perception of beauty? Has age have any role in this? Psychological and art studies reveal striking differences in perception among various demographical aspects. This warrants attention particularly with the rapid growth in automatic evaluation of photo aesthetics. In this research, we investigate the influences of demographic factors of photographer towards the aesthetic quality of portrait photos from the computational perspective. An extended version of the large-scale AVA dataset was created with the inclusion of the photographers’ demographic data such as location, age and gender. We trained several demographic-centric CNN models, which are then fused together as a single multi-demographic CNN model to learn aesthetic tendencies in a holistic manner. We demonstrate the efficacy of our model in achieving state-of-the-art performance in predicting portraiture aesthetics.
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Notes
- 1.
The authors of the AVA dataset [10] introduced the parameter \(\delta \) to filter ambiguous photos during the training step; photos with average rating \([5-\delta ,5+\delta ]\) are omitted. \(\delta =0\) in the testing step.
- 2.
AVA-Portraits+ demographic meta-data is available at https://goo.gl/jo13f3.
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Kairanbay, M., See, J., Wong, LK. (2018). Towards Demographic-Based Photographic Aesthetics Prediction for Portraitures. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_43
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