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Information × Registration Number 2120U007871, Article popup.category Препринт Title Accuracy And Bias Of Selfie Detection On Open Data (AI translated) popup.author Shpot Natalia-YanaShpot Natalia-Yana popup.publication 01-01-2020 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4515 popup.publisher Description There are many challenges related to the openness of the Wikimedia Commons im- age upload platform, and one of them is about making sure to get high-quality con- tent in. Goes without saying, selfies are not precisely the ideal wanted content for a platform whose aim is to represent the world’s knowledge through pictorial rep- resentations. One way to automatically check the data quality in the domain of computer vision is to design a selfie detector that, given an image, can automatically predict whether it is a selfie or not. Thus in this thesis, we are using state-of-the-art models to create a classifier that, given an image, can say whether the image is a selfie, a person, or neither of that. With such a classifier, it would be easier to auto- matically detect and scale selfies for Wikimedia or other platforms that have humans in the loop to check the quality of user-generated content. In addition to this we ex- amine whether approaches of our choice show bias in demographics such as race, gender, and age. Furthermore, we will introduce two datasets for our project: one containing selfies, pictures with persons and random pictures, and another contain- ing a smaller set of pictures of persons along with the demographic metadata. popup.nrat_date 2025-11-05 Close