A Robust Driver Emotion Recognition Method Based on High-Purity Feature Separation

Since emotions generally affect driver's behavior, judgment, and reaction time, accurately identifying driver's emotions is of great significance to improve the safety and comfort of intelligent driving system. However, the gender, skin color, age, and appearance of different drivers often...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-12, Vol.24 (12), p.15092-15104
Main Authors: Yang, Lie, Yang, Haohan, Hu, Bin-Bin, Wang, Yan, Lv, Chen
Format: Article
Language:English
Subjects:
Datasets
Driver emotion recognition
Emotion recognition
Emotions
Face recognition
Feature extraction
feature separation
Illumination
illumination changes
individual difference
Interference
Lighting
Luminous intensity
Physiology
Purity
Robustness
Separation
Task analysis
Vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Since emotions generally affect driver's behavior, judgment, and reaction time, accurately identifying driver's emotions is of great significance to improve the safety and comfort of intelligent driving system. However, the gender, skin color, age, and appearance of different drivers often have big differences, which will greatly interfere with the emotional recognition process. Besides, light intensity inside the vehicle varies with different time, weather, and location, which will also pose a challenge to driver emotion recognition. In this paper, a robust driver emotion recognition method based on feature separation is proposed to overcome the interference of individual differences and illumination changes. In order to realize the separation of expression-related features and irrelevant features, we design a high-purity feature separation (HPFS) framework based on partial feature exchange and the constraints of multiple loss functions. To verify that the proposed method can overcome the interference of illumination changes, we specifically create a multiple light intensities driver emotion recognition (MLI-DER) dataset and conduct a great deal of experiments on the dataset. In addition, to further demonstrate that our method can largely alleviate the interference of individual difference, some cross-subject emotion recognition experiments are conducted on two famous facial expression recognition datasets FACES and Oulu-CASIA and the experimental results are compared with that of some state-of-the-art methods.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3304128