A presentation on facial expression recognition using deep learning. This is based off a survey posted on Medium: https://medium.com/@emmelinetsen/facial-expression-recognition-using-deep-learning-3ec1d7426604

1. Facial Expression Recognition
(FER) using Deep Learning
Emmeline Tsen
Medium article: https://medium.com/@emmelinetsen/facial-expression-recognition-using-deep-learning-
3ec1d7426604?sk=72c845275071df13dccc97bd07a73830

2. Conventional FER Approaches
● Lots of manual feature engineering
● Images need to be preprocessed
● Need to select appropriate feature extraction and classification method for
output
○ Both separated into two components
Conventional FER Approach Workflow

3. Deep Learning Approaches
● More dependent on data and hardware
● More robust to its environments
● Three proposed ways for FER
○ Convolutional Neural Network (CNN)
○ Long Short Term Memory (LSTM)
○ Generative Adversarial Network (GAN)

4. Convolutional Neural Network (CNN)

5. Long Short Term Memory (LSTM)
● Recurrent neural network (RNN)
● Extracts temporal features within consecutive frames
● Recommended for video sequences

6. Generative Adversarial Network (GAN)
● Consists of a generative network and discriminative network
● Generates images
● Synthesizes facial images to make them look realistic
● Able to increase FER training dataset
○ Automatically generating images

7. Metrics
● Numerous testing and training datasets for FER
● Best represent advantages of using deep learning based FER models

8. Performance of FER using CNN & LSTM on MMI and CK+ Datasets
Performance of FER using CNN & GAN on Oulu-CASIA, BU-3DFE, and
Multi-PIE Datasets

9. Final Thoughts
● FER has been receiving a lot of attention
● Different approaches of FER
● There are still challenges & lots of opportunities to evaluate different
algorithms

10. References
● Huang, Y., Chen, F., Lv, S., & Wang, X. (2019, September 20). Facial Expression Recognition: A Survey. Retrieved May 8, 2020,
from https://www.mdpi.com/2073-8994/11/10/1189
● Divya, M., Reddy, R. O., & Raghavendra, C. (2019). Effective Facial Emotion Recognition using Convolutional Neural Network
Algorithm. International Journal of Recent Technology and Engineering Regular Issue, 8(4), 4351–4354.
doi:10.35940/ijrte.d8275.118419
● Amidi, A., & Amidi, S. (n.d.). Convolutional Neural Networks cheatsheet Star. Retrieved May 8, 2020, from
https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks