  Online dating has become a common occurrence over the last few decades. A key
challenge for online dating platforms is to determine suitable matches for
their users. A lot of dating services rely on self-reported user traits and
preferences for matching. At the same time, some services largely rely on user
images and thus initial visual preference. Especially for the latter approach,
previous research has attempted to capture users' visual preferences for
automatic match recommendation. These approaches are mostly based on the
assumption that physical attraction is the key factor for relationship
formation and personal preferences, interests, and attitude are largely
neglected. Deep learning approaches have shown that a variety of properties can
be predicted from human faces to some degree, including age, health and even
personality traits. Therefore, we investigate the feasibility of bridging
image-based matching and matching with personal interests, preferences, and
attitude. We approach the problem in a supervised manner by predicting
similarity scores between two users based on images of their faces only. The
ground-truth for the similarity matching scores is determined by a test that
aims to capture users' preferences, interests, and attitude that are relevant
for forming romantic relationships. The images are processed by a Siamese
Multi-Task deep learning architecture. We find a statistically significant
correlation between predicted and target similarity scores. Thus, our results
indicate that learning similarities in terms of interests, preferences, and
attitude from face images appears to be feasible to some degree.
