Towards Demographic-Based Photographic Aesthetics Prediction for Portraitures

Magzhan Kairanbay*, John See, Lai Kuan Wong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMultiMedia Modeling. MMM 2018
Number of pages13
ISBN (Electronic)9783319736037
ISBN (Print)9783319736020
Publication statusPublished - 13 Jan 2018
Event24th International Conference on MultiMedia Modeling 2018 - Bangkok, Thailand
Duration: 5 Feb 20187 Feb 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on MultiMedia Modeling 2018
Abbreviated titleMMM 2018

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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