This paper presents a method which estimates interest level while watching videos, based on collaborative use of facial expression and biological signals such as electroencephalogram (EEG) and electrocardiogram (ECG). To the best of our knowledge, no studies have been carried out on the collaborative use of facial expression and biological signals for estimating interest level. Since training data, which is used for estimating interest level, is generally small and imbalanced, Variational Bayesian Mixture of Robust Canonical Correlation Analysis (VBMRCCA) is newly applied to facial expression and biological signals, which are obtained from users while they are watching the videos. Unlike some related works, VBMRCCA is used to obtain the posterior distributions which represent the latent correlation between facial expression and biological signals in our method. Then, the users' interest level can be estimated by comparing the posterior distributions of the positive class data with those of the negative. Consequently, successful interest level estimation, via collaborative use of facial expression and biological signals, becomes feasible.
['Yuma Sasaka', 'Takahiro Ogawa', 'Miki Haseyama']
Multimodal Interest Level Estimation via Variational Bayesian Mixture of Robust CCA