Estimation of Emotions Evoked by Images Based on Multiple Gaze-based CNN Features
Taiga Matsui, Naoki Saito, Takahiro Ogawa, Satoshi Asamizu, Miki Haseyama · 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) · 2019
This paper presents a method for estimating emotions evoked by watching images based on multiple visual features considering relationship with gaze information. The proposed method obtains multiple visual features from multiple middle layers of a Convolutional Neural Network. Then the proposed method newly derives their gaze-based visual features maximizing correlation with gaze information by using Discriminative Locality Preserving Canonical Correlation Analysis. The final estimation result is calculated by integrating multiple estimation results obtained from these gaze-based visual features. Consequently, successful emotion estimation becomes feasible by using such multiple estimation results which correspond to different semantic levels of target images.