  Face images are one of the main areas of focus for computer vision, receiving
on a wide variety of tasks. Although face recognition is probably the most
widely researched, many other tasks such as kinship detection, facial
expression classification and facial aging have been examined. In this work we
propose the new, subjective task of quantifying perceived face similarity
between a pair of faces. That is, we predict the perceived similarity between
facial images, given that they are not of the same person. Although this task
is clearly correlated with face recognition, it is different and therefore
justifies a separate investigation. Humans often remark that two persons look
alike, even in cases where the persons are not actually confused with one
another. In addition, because face similarity is different than traditional
image similarity, there are challenges in data collection and labeling, and
dealing with diverging subjective opinions between human labelers. We present
evidence that finding facial look-alikes and recognizing faces are two distinct
tasks. We propose a new dataset for facial similarity and introduce the
Lookalike network, directed towards similar face classification, which
outperforms the ad hoc usage of a face recognition network directed at the same
task.
