# Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks

## TL;DR

This paper introduces an advanced 3D CNN combined with LSTM and facial landmarks for improved video-based facial expression recognition, demonstrating superior performance across multiple datasets.

## Contribution

It presents a novel 3D Inception-ResNet and LSTM architecture that effectively captures spatial and temporal features for FER, incorporating facial landmarks for enhanced accuracy.

## Key findings

- Outperforms state-of-the-art methods on four datasets
- Effective in subject-independent and cross-database tasks
- Highlights importance of facial landmarks in FER

## Abstract

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.