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Face images are one of the main areas of focus for computer vision, receivingon a wide variety of tasks. Although face recognition is probably the mostwidely researched, many other tasks such as kinship detection, facialexpression classification and facial aging have been examined. In this work wepropose the new, subjective task of quantifying perceived face similaritybetween a pair of faces. That is, we predict the perceived similarity betweenfacial images, given that they are not of the same person. Although this taskis clearly correlated with face recognition, it is different and thereforejustifies a separate investigation. Humans often remark that two persons lookalike, even in cases where the persons are not actually confused with oneanother. In addition, because face similarity is different than traditionalimage similarity, there are challenges in data collection and labeling, anddealing with diverging subjective opinions between human labelers. We presentevidence that finding facial look-alikes and recognizing faces are two distincttasks. We propose a new dataset for facial similarity and introduce theLookalike network, directed towards similar face classification, whichoutperforms the ad hoc usage of a face recognition network directed at the sametask.

Finding your Lookalike: Measuring Face Similarity Rather than Face Identity