Arousal Classification on MAHNOB-HCI
83.84AccuracyAli et al. (2025)
Evaluation results
| Method | Links | |
|---|---|---|
| Ali et al. (2025)Modalities=Facial, Bio-Sensing, Features=Face appearance, Physiological image-based2026.05 | 83.84 | |
| Oğuz et al. (2023)Modalities=ECG, Features=Morphological, HRV2026.05 | 83.61 | |
| Siddharth et al. (2019)Modalities=Facial, Bio-Sensing, Features=Face appearance, PSD, Physiological image-based etc.2026.05 | 82.93 | |
| Tan et al. (2021)Modalities=Facial, EEG, Features=Facial landmarks, EEG deep learning-based2026.05 | 79.39 | |
| Li et al. (2021)Modalities=Facial, EEG, Features=Face appearance, PSD2026.05 | 77.22 | |
| Zhu et al. (2024)Modalities=Facial, EEG, Features=Face appearance, PSD2026.05 | 75.62 | |
| SCPTModalities=Facial, rPPG, Features=Face appearance, rPPG deep learning-based2026.05 | 74.17 | |
| Wu et al. (2026)Modalities=Facial, EEG, Features=Face appearance, EEG deep learning-based2026.05 | 73.78 | |
| Ferdinando et al. (2018)Modalities=ECG, Features=HRV2026.05 | 73.5 | |
| CAModalities=Facial, rPPG, Features=Face appearance, rPPG deep learning-based2026.05 | 70.83 | |
| Singh et al. (2023)Modalities=Bio-Sensing, Features=Bio-sensing deep learning-based2026.05 | 68.7 | |
| Li et al. (2024)Modalities=Facial, rPPG, Features=Face appearance, rPPG deep learning-based2026.05 | 66.83 | |
| CAPModalities=Facial, rPPG, Features=Face appearance, rPPG deep learning-based2026.05 | 66.67 | |
| Jia et al. (2024)Modalities=Bio-Sensing, Features=Bio-sensing deep learning-based2026.05 | 66.3 | |
| Mellouk et al. (2023)Modalities=rPPG, Features=rPPG deep learning-based2026.05 | 60 |