Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition

Introduction

Facial expression is one of the most natural ways that human beings express emotion. The Facial Action Coding System (FACS) attempts to systemically categorize each facial expression by specifying a basic set of muscle contractions or relaxations, formally called Action Units (AUs). For example, "AU 1" stands for the inner portion of the brows being raised, and "AU 6" stands for the cheeks. Such a framework would help us in denoting any facial expression as possibly a combination of different AUs.

However, during the course of an average day, most human beings do not experience drastically varying emotions and therefore their facial expressions might change only subtly. Additionally, there might also be a lot of subjectivity involved if this task were to be done manually. To address these issues, it is imperative to automate this task. Moreover, automating AU recognition also has potential applications in human-computer interaction (HCI), online education, interactive gaming, among other fields.

Because of the recent advancements in object detection and categorization tasks, CNNs are an appealing go-to for the facial AU recognition task described above. However, because of the small size of training sets available, the learned CNNs suffer from overfitting. To overcome

References

1. Han, S., Meng, H., Khan, A. S., Tong, Y. (2016) "Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition". NIPS. 2. Tian, Y., Kanade, T., Cohn, J. F. (2001) "Recognizing Action Units for Facial Expression Analysis". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23., No. 2.