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

arXiv: https://arxiv.org/abs/1707.08630 · HTML: https://mlsanity.com/paper/1707.08630/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes](https://mlsanity.com/paper/2110.08059/) (2021)
- [Dynamic Sparse Network for Time Series Classification: Learning What to "see''](https://mlsanity.com/paper/2212.09840/) (2022)
- [Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection](https://mlsanity.com/paper/2308.07770/) (2023)
- [Meta Auxiliary Learning for Facial Action Unit Detection](https://mlsanity.com/paper/2105.06620/) (2021)
- [AdaFSNet: Time Series Classification Based on Convolutional Network with a Adaptive and Effective Kernel Size Configuration](https://mlsanity.com/paper/2404.18246/) (2024)
- [Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition](https://mlsanity.com/paper/2106.02258/) (2021)

## Abstract (arXiv)

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. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm.