Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings
This provides an incremental improvement for Tinder users seeking to automate profile screening based on facial attractiveness.
The researchers tackled the problem of automatically classifying Tinder dating profiles based on user preferences by using FaceNet facial embeddings as features, achieving 73% accuracy with just 80 profiles before hitting diminishing returns.
A method to produce personalized classification models to automatically review online dating profiles on Tinder is proposed, based on the user's historical preference. The method takes advantage of a FaceNet facial classification model to extract features which may be related to facial attractiveness. The embeddings from a FaceNet model were used as the features to describe an individual's face. A user reviewed 8,545 online dating profiles. For each reviewed online dating profile, a feature set was constructed from the profile images which contained just one face. Two approaches are presented to go from the set of features for each face, to a set of profile features. A simple logistic regression trained on the embeddings from just 20 profiles could obtain a 65% validation accuracy. A point of diminishing marginal returns was identified to occur around 80 profiles, at which the model accuracy of 73% would only improve marginally after reviewing a significant number of additional profiles.