# Learning Grimaces by Watching TV

Source: [Learning Grimaces by Watching TV](<https://www.sota2.com/research/paper/68fa62d41e209131ce9fb6df>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-25T02:29:40.786Z

Authors: Samuel Albanie, Andrea Vedaldi

Published: 2016-10-07T12:42:47.000Z

## Abstract

Differently from computer vision systems which require explicit supervision, humans can learn facial expressions by observing people in their environment. In this paper, we look at how similar capabilities could be developed in machine vision. As a starting point, we consider the problem of relating facial expressions to objectively measurable events occurring in videos. In particular, we consider a gameshow in which contestants play to win significant sums of money. We extract events affecting the game and corresponding facial expressions objectively and automatically from the videos, obtaining large quantities of labelled data for our study. We also develop, using benchmarks such as FER and SFEW 2.0, state-of-the-art deep neural networks for facial expression recognition, showing that pre-training on face verification data can be highly beneficial for this task. Then, we extend these models to use facial expressions to predict events in videos and learn nameable expressions from them. The dataset and emotion recognition models are available at http://www.robots.ox.ac.uk/~vgg/data/facevalue

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Facial Expression Recognition on FER Public 2013 (test)](<https://www.sota2.com/research/sota/facial-expression-recognition-on-fer-public-2013-test.md>): Facial Expression Recognition; FER Public 2013 (test); Accuracy: 72.05.

- [Facial Expression Recognition on FER Private 2013 (test)](<https://www.sota2.com/research/sota/facial-expression-recognition-on-fer-private-2013-test.md>): Facial Expression Recognition; FER Private 2013 (test); Accuracy: 72.89.

- [Facial Expression Recognition on SFEW 2.0 (test)](<https://www.sota2.com/research/sota/facial-expression-recognition-on-sfew-2-0-test.md>): Facial Expression Recognition; SFEW 2.0 (test); Accuracy: 59.41.

- [Facial Expression Recognition on SFEW 2.0 (val)](<https://www.sota2.com/research/sota/facial-expression-recognition-on-sfew-2-0-val.md>): Facial Expression Recognition; SFEW 2.0 (val); Accuracy: 54.82.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1610.02255>)

[PDF](<https://arxiv.org/pdf/1610.02255.pdf>)

[Implementation](<http://www.robots.ox.ac.uk/vgg/data/facevalue>)