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.

arXiv comment6 pages, Humanoids 2017 IEEE RAS International Conference workshop Cooperative Autonomous Robot Experience (Presentation)

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Going Deeper with Convolutions
• Return of the Devil in the Details: Delving Deep into Convolutional Nets

Cited by in corpus (1)

• Interpretable Deep Neural Networks for Facial Expression and Dimensional Emotion Recognition in-the-wild