I Know How You Feel: Emotion Recognition with Facial Landmarks

TL;DR

This paper introduces EmotionalDAN, a novel emotion recognition model that incorporates facial landmarks into the training process, outperforming existing methods on benchmark datasets by up to 5%.

Contribution

It proposes a new approach that explicitly uses facial landmarks in the classification loss, enhancing emotion recognition accuracy.

Findings

Outperforms state-of-the-art emotion classification methods by up to 5%.

Extends Deep Alignment Network with facial feature-based loss.

Demonstrates effectiveness on benchmark datasets.

Abstract

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.