Video preference estimation using fNIRS signals
Akira Toyoda, Takahiro Ogawa, Miki Haseyama · 2017
This paper presents a new method for video preference estimation using functional near-infrared spectroscopy signals (fNIRS signals). The proposed method first computes fNIRS features from fNIRS signals recorded while users are watching videos and multiple visual features from these videos. Next, by applying Locality Preserving Canonical Correlation Analysis to fNIRS features and each visual feature, we can obtain multiple new visual features. In addition, Multiview Local Fisher Discriminant Analysis fuses multiple new visual features and optimizes within and between class scatter in the fused feature space while using complementary properties in these features. Consequently, we can realize video preference estimation by using the fused features.