# Facial Expression Recognition from World Wild Web

arXiv: https://arxiv.org/abs/1605.03639 · HTML: https://mlsanity.com/paper/1605.03639/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2016.

## Built on by (most cited first, 6 of 6)

- [An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild](https://mlsanity.com/paper/2107.03465/) (2021)
- [Teacher-Student Training and Triplet Loss to Reduce the Effect of Drastic Face Occlusion](https://mlsanity.com/paper/2111.10561/) (2021)
- [AffectNet+: A Database for Enhancing Facial Expression Recognition with Soft-Labels](https://mlsanity.com/paper/2410.22506/) (2024)
- [Affective Video Content Analysis: Decade Review and New Perspectives](https://mlsanity.com/paper/2310.17212/) (2023)
- [Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark](https://mlsanity.com/paper/2109.11243/) (2021)
- [Modular Deep Learning Framework for Assistive Perception: Gaze, Affect, and Speaker Identification](https://mlsanity.com/paper/2511.20474/) (2025)

## Abstract (arXiv)

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%.