Transfer Learning for Action Unit Recognition
Abstract
This paper presents a classifier ensemble for Facial Expression Recognition (FER) based on models derived from transfer learning. The main experimentation work is conducted for facial action unit detection using feature extraction and fine-tuning convolutional neural networks (CNNs). Several classifiers for extracted CNN codes such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) and Long Short-Term Memory (LSTM) are compared and evaluated. Multi-model ensembles are also used to further improve the performance. We have found that VGG-Face and ResNet are the relatively optimal pre-trained models for action unit recognition using feature extraction and the ensemble of VGG-Net variants and ResNet achieves the best result.
Keywords
Cite
@article{arxiv.1807.07556,
title = {Transfer Learning for Action Unit Recognition},
author = {Yen Khye Lim and Zukang Liao and Stavros Petridis and Maja Pantic},
journal= {arXiv preprint arXiv:1807.07556},
year = {2018}
}
Comments
6 pages, Humanoids 2017 IEEE RAS International Conference workshop Cooperative Autonomous Robot Experience (Presentation)