Finding your Lookalike: Measuring Face Similarity Rather than Face Identity

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 task of face similarity. That is, we predict the perceived similarity between facial images, given that they are \textit{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 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 that is directed towards similar face classification and outperforms the ad hoc usage of usage of a face recognition network directed at the same task.

• Machine Perception