Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units
This work addresses emotion recognition in computer vision, which is important for applications like human-computer interaction, but it appears incremental as it builds on existing deep learning approaches without a major breakthrough.
The authors tackled facial emotion recognition by proposing two methods: an autoencoder for emotion representation and an 8-layer CNN, trained on the JAFFE dataset and tested on LFW images. Their CNN model showed potential to outperform state-of-the-art methods with more fine-tuning and depth.
Emotion being a subjective thing, leveraging knowledge and science behind labeled data and extracting the components that constitute it, has been a challenging problem in the industry for many years. With the evolution of deep learning in computer vision, emotion recognition has become a widely-tackled research problem. In this work, we propose two independent methods for this very task. The first method uses autoencoders to construct a unique representation of each emotion, while the second method is an 8-layer convolutional neural network (CNN). These methods were trained on the posed-emotion dataset (JAFFE), and to test their robustness, both the models were also tested on 100 random images from the Labeled Faces in the Wild (LFW) dataset, which consists of images that are candid than posed. The results show that with more fine-tuning and depth, our CNN model can outperform the state-of-the-art methods for emotion recognition. We also propose some exciting ideas for expanding the concept of representational autoencoders to improve their performance.