Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction

In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation's ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (DPIP) have been very inaccurate, unadaptable to real-world application, and have only taken into account a subject's physical attractiveness. To that effect, we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart, trustworthy, and attractive the subject appears in highly variable dating photos. Our "attractive" output is also applicable to Facial Beauty Prediction (FBP), making Photofeeler-D3 state-of-the-art for both DPIP and FBP. We achieve this by leveraging Photofeeler's Dating Dataset (PDD) with over 1 million images and tens of millions of votes, our novel technique of voter modeling, and cutting-edge computer vision techniques.

Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Rating

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.