Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories

TL;DR

This paper introduces a novel method for facial expression recognition using deep covariance descriptors and trajectories on the SPD manifold, achieving state-of-the-art results on multiple datasets.

Contribution

It presents a new approach that encodes CNN features into covariance descriptors and models temporal dynamics as deep trajectories on the SPD manifold.

Findings

Deep covariance descriptors outperform standard CNN classifiers.

Modeling temporal dynamics as deep trajectories improves recognition accuracy.

Achieves state-of-the-art performance on multiple facial expression datasets.

Abstract

In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Convolutional Neural Network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of Symmetric Positive Definite (SPD) matrices. By conducting the classification of static facial expressions using Support Vector Machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that both the proposed static and dynamic approaches achieve state-of-the-art performance for facial expression recognition outperforming many recent approaches.