Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Rating
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Online dating has gained substantial popularity in the last twenty years, making picking one's best dating profile photos more vital than ever before. To that effect, we propose Photofeeler-D3 - the first convolutional neural network to rate dating photos for how smart, trustworthy, and attractive the subject appears. We name this task Dating Photo Rating (DPR). Leveraging Photofeeler's Dating Dataset (PDD) with over 1 million images and tens of millions of votes, Photofeeler-D3 achieves a 28% higher correlation to human votes than existing online AI platforms for DPR. We introduce the novel concept of voter modeling and use it to achieve this benchmark. The "attractive" output of our model can also be used for Facial Beauty Prediction (FBP) and achieve state-of-the-art results. Without training on a single image from the HotOrNot dataset, we achieve 10% higher correlation than any model from literature. Finally, we demonstrate that Photofeeler-D3 achieves approximately the same correlation as 10 unnormalized and unweighted human votes, making it the state-of-the-art for both tasks: DPR and FBP.
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