The analysis of facial beauty: an emerging area of research in pattern analysis

Much research presented recently supports the idea that the human perception of attractiveness is data-driven and largely irrespective of the perceiver. This suggests using pattern analysis techniques for beauty analysis. Several scientific papers on this subject are appearing in image processing, computer vision and pattern analysis contexts, or use techniques of these areas. In this paper, we will survey the recent studies on automatic analysis of facial beauty, and discuss research lines and practical application

Developing a Sufficient Knowledge Base for Faces: Implicit Recognition Memory for Distinctive versus Typical Female Faces

Research on adults' face recognition abilities provides evidence for a distinctiveness effect such that distinctive faces are remembered better and more easily than typical faces. Research on this effect in the developmental literature is limited. In the current study, two experiments tested recognition memory for evidence of the distinctiveness effect. Study 1 tested infants (9- and 10-month olds) using a novelty preference paradigm. Infants were tested for immediate and delayed memory. Results indicated memory for only the most distinctive faces. Study 2 tested preschool children (3- and 4-year-olds) using an interactive story. Children were tested with an implicit (i.e. surprise) memory test. Results indicated a memory advantage for distinctive faces by three-year-old girls and four-year-old boys and girls. Contrary to traditional theories of changes in children's processing strategies, experience is also a critical factor in the development of face recognition abilities

Wearing Many (Social) Hats: How Different are Your Different Social Network Personae?

This paper investigates when users create profiles in different social networks, whether they are redundant expressions of the same persona, or they are adapted to each platform. Using the personal webpages of 116,998 users on About.me, we identify and extract matched user profiles on several major social networks including Facebook, Twitter, LinkedIn, and Instagram. We find evidence for distinct site-specific norms, such as differences in the language used in the text of the profile self-description, and the kind of picture used as profile image. By learning a model that robustly identifies the platform given a user's profile image (0.657--0.829 AUC) or self-description (0.608--0.847 AUC), we confirm that users do adapt their behaviour to individual platforms in an identifiable and learnable manner. However, different genders and age groups adapt their behaviour differently from each other, and these differences are, in general, consistent across different platforms. We show that differences in social profile construction correspond to differences in how formal or informal the platform is.Comment: Accepted at the 11th International AAAI Conference on Web and Social Media (ICWSM17