# Facial Affect Estimation in the Wild Using Deep Residual and Convolutional Networks

## TL;DR

This paper introduces three deep neural network models based on Inception-ResNet modules for facial affect estimation in unconstrained environments, achieving competitive accuracy in the Affect-in-the-Wild challenge.

## Contribution

It proposes novel neural network architectures tailored for facial affect estimation, combining Inception-ResNet modules with LSTMs and multi-scale feature extraction.

## Key findings

- Achieved RMSE of 0.4 for valence and 0.3 for arousal
- Attained CCC of 0.04 for valence and 0.29 for arousal
- Demonstrated effectiveness of redesigned Inception-ResNet modules in affect estimation

## Abstract

Automated affective computing in the wild is a challenging task in the field of computer vision. This paper presents three neural network-based methods proposed for the task of facial affect estimation submitted to the First Affect-in-the-Wild challenge. These methods are based on Inception-ResNet modules redesigned specifically for the task of facial affect estimation. These methods are: Shallow Inception-ResNet, Deep Inception-ResNet, and Inception-ResNet with LSTMs. These networks extract facial features in different scales and simultaneously estimate both the valence and arousal in each frame. Root Mean Square Error (RMSE) rates of 0.4 and 0.3 are achieved for the valence and arousal respectively with corresponding Concordance Correlation Coefficient (CCC) rates of 0.04 and 0.29 using Deep Inception-ResNet method.