Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm

TL;DR

This paper introduces a novel deep-rooted learning algorithm for micro-facial expression recognition that outperforms existing classifiers by effectively extracting and learning from micro-facial features, even under challenging conditions.

Contribution

The paper proposes a new MFEDRL classifier with dual loss functions, improving micro-expression recognition accuracy over traditional deep learning models.

Findings

Outperforms CNN, DNN, ANN, SVM, KNN in accuracy and MAE

Uses adaptive filtering for face detection and rotation correction

Effective in recognizing spontaneous micro-expressions

Abstract

Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three major steps are involved to improve the performance of micro-facial expression recognition. First, an Adaptive Homomorphic Filtering is used for face detection and rotation rectification processes. Secondly, Micro-facial features were used to extract the appearance variations of a testing image-spatial analysis. The features of motion information are used for expression recognition in a sequence of facial images. An effective Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is proposed in this paper to better recognize spontaneous micro-expressions by learning parameters on the optimal features. This proposed method includes two loss functions such as cross entropy loss function and centre loss function. Then the performance of the algorithm will be evaluated using recognition rate and false measures. Simulation results show that the predictive performance of the proposed method outperforms that of the existing classifiers such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbours (KNN) in terms of accuracy and Mean Absolute Error (MAE).