# Finding your Lookalike: Measuring Face Similarity Rather than Face Identity

arXiv: https://arxiv.org/abs/1806.05252 · HTML: https://mlsanity.com/paper/1806.05252/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [A View From Somewhere: Human-Centric Face Representations](https://mlsanity.com/paper/2303.17176/) (2023)
- [Benchmarking Human Face Similarity Using Identical Twins](https://mlsanity.com/paper/2208.11822/) (2022)
- [Learning an Adaptation Function to Assess Image Visual Similarities](https://mlsanity.com/paper/2206.01417/) (2022)
- [An Image-based Generator Architecture for Synthetic Image Refinement](https://mlsanity.com/paper/2108.04957/) (2021)
- [HAIFAI: Human-AI Interaction for Mental Face Reconstruction](https://mlsanity.com/paper/2412.06323/) (2024)

## Abstract (arXiv)

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.