  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.
