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arXiv:1807.07556 [cs.CV]AbstractReferencesReviewsResources

Transfer Learning for Action Unit Recognition

Yen Khye Lim, Zukang Liao, Stavros Petridis, Maja Pantic

Published 2018-07-19Version 1

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.

Comments: 6 pages, Humanoids 2017 IEEE RAS International Conference workshop Cooperative Autonomous Robot Experience (Presentation)
Categories: cs.CV
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