Facial Expression Recognition from World Wild Web
This work addresses facial expression recognition in wild settings, which is important for applications like human-computer interaction, but it is incremental as it applies existing methods to new noisy data.
The study tackled the problem of recognizing facial expressions in uncontrolled web images by collecting and annotating a dataset using search queries, and found that deep neural networks achieved an accuracy of 82.12%.
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%.