Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Understand

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem.

• Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed.
• In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation.
• Such a training process suffers from expensive training cost, especially as the network becomes deeper.