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

• 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
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3304128

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.