How smart does your profile image look? Estimating intelligence from social network profile images

Profile images on social networks are users’ opportunity topresent themselves and to affect how others judge them. Weexamine what Facebook images say about users’ perceivedand measured intelligence. 1,122 Facebook users completeda matrices intelligence test and shared their current Facebookprofile image. Strangers also rated the images for perceivedintelligence. We use automatically extracted imagefeatures to predict both measured and perceived intelligence.Intelligence estimation from images is a difficult task evenfor humans, but experimental results show that human accuracycan be equalled using computing methods. We reportthe image features that predict both measured and perceivedintelligence, and highlight misleading features suchas "smiling” and "wearing glasses” that are correlated withperceived but not measured intelligence. Our results give insightsinto inaccurate stereotyping from profile images andalso have implications for privacy, especially since in mostsocial networks profile images are public by default

How smart does your profile image look? Estimating intelligence from social network profile images

Profile images on social networks are users' opportunity to present themselves and to affect how others judge them. We examine what Facebook images say about users' perceived and measured intelligence. 1,122 Facebook users completed a matrices intelligence test and shared their current Facebook profile image. Strangers also rated the images for perceived intelligence. We use automatically extracted image features to predict both measured and perceived intelligence. Intelligence estimation from images is a difficult task even for humans, but experimental results show that human accuracy can be equalled using computing methods. We report the image features that predict both measured and perceived intelligence, and highlight misleading features such as "smiling'' and "wearing glasses'' that are correlated with perceived but not measured intelligence. Our results give insights into inaccurate stereotyping from profile images and also have implications for privacy, especially since in most social networks profile images are public by default

Deep representations to model user ‘Likes’

© Springer International Publishing Switzerland 2015. Automatically understanding and modeling a user’s likingfor an image is a challenging problem. This is because the relationshipbetween the images features (even semantic ones extracted by existingtools, viz. faces, objects etc.) and users’ ‘likes’ is non-linear, influenced by several subtle factors. This work presents a deep bi-modal knowledge representation of images based on their visual content and associated tags(text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. It also includes feature selection before learning deep representation to identify the important features for a user to like an image. Then the proposed representation is shown to be effective in learning a model of users image ‘likes’ based on a collection of images ‘liked’ by him. On a collection of images ‘liked’ by users (from Flickr) the proposed deep representation is shown to better state-of-art low-level features used for modeling user ‘likes’ by around 15–20 %