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Based on the foundation architecture of the CNN, some variations have also been applied to solve the problem of Facial Expression Recognition, including Deep convolutional neural network (DCNN) – a feedforward neural network that applies back propagation algorithms to adjust the parameters (weights and biases), AlexNet, Inception, Residual Neural Network (Resnet), and 2-Channel CNN where one channel is a standard CNN network and another is trained as a Convolutional Autoencoder, or in which each channel trains different facial parts.
However, having a large number of parameters is the main problem of those networks. This makes them impracticable to be trained on small datasets. Besides, they also require long training/test time and large memory cost. |
IEEE/ICACT20210175 Slide.22
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