Implementation of Classical Methods
We have implemented three methods of performing facial emotion recognition proposed in
reference papers. The methods implemented include using Shallow Neural Networks, Deep
Neural Networks and simple Convolutional Neural Networks(CNN).
Shallow Neural Networks show the shortest training time due to the lower number of training
parameters, however they also have the lowest accuracy(33.4%). Deep Neural Networks take
slightly longer to train but show a slight increase in accuracy(38.1%) over the same number
of epochs.
Convolutional Neural Networks, which are commonly used for image recognition tasks take
the longest amount of time to train but show the highest accuracy(57.1%). The simple CNN
shows a high degree of overfitting. Some solutions include normalising the input or using
dropout when training.
We can conclude that CNNs are more suited for accomplishing the task of Facial Emotion
Recognition than Shallow Neural Networks and Deep Neural Networks.
Member
Contribution
Literature
Proposed
Approach
implemented
methodology
Arvind Prabhu
https://arxiv.org/ CNN approach CNN approach
Implemented
CNN approach pdf/1910.05602.
pdf
Prasanna
Kumar
R D Kathik
Implemented
Deep Neural
Network
Approach
https://arxiv.org/ Deep Neural
pdf/1910.05602. Network
pdf
Approach
Deep Neural
Network
Approach
https://arxiv.org/ Shallow Neural Shallow Neural
Implemented
Shallow Neural pdf/1910.05602. Network
Network
pdf
Network
Approach
Approach
Approach
Method 1(Feedforward Neural Network):
Method 2(Deep Neural Network):
Method 3(Convolutional Neural Network):