A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
This work addresses the challenge of multi-pose facial expression recognition for computer vision applications, but it is incremental as it builds on existing deep learning methods with a new dataset and synthesis approach.
The authors tackled the problem of facial expression recognition across varied poses by creating a new dataset of over 200k images with 119 persons, 4 poses, and 54 expressions, and proposed a framework using a generative adversarial network for data augmentation and a LightCNN-based model for classification, achieving validation of effectiveness in experiments.
The recent research of facial expression recognition has made a lot of progress due to the development of deep learning technologies, but some typical challenging problems such as the variety of rich facial expressions and poses are still not resolved. To solve these problems, we develop a new Facial Expression Recognition (FER) framework by involving the facial poses into our image synthesizing and classification process. There are two major novelties in this work. First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions. To our knowledge this is the first dataset to label faces with subtle emotion changes for expression recognition purpose. It is also the first dataset that is large enough to validate the FER task on unbalanced poses, expressions, and zero-shot subject IDs. Second, we propose a facial pose generative adversarial network (FaPE-GAN) to synthesize new facial expression images to augment the data set for training purpose, and then learn a LightCNN based Fa-Net model for expression classification. Finally, we advocate four novel learning tasks on this dataset. The experimental results well validate the effectiveness of the proposed approach.