VFL—A deep learning-based framework for classifying walking gaits into emotions

Abstract

Human emotion is perceived not only in facial expressions but also in every kind of body language, including a human’s walking gait. In this paper, we propose a VFL framework for classifying a human’s walking gait into emotions. This framework introduces deep learning methods, which are merely applied to gait data, as the main methods for performing emotion recognition tasks with walking gaits. First, we obtain gait movement data from original walking videos or records and use the gait data that contain only body keypoint positions as input. Then, we expand the data to other kinetic features, rebuild the main skeleton in images, and extract vision features from the images. Based on the data and fusion features, we perform feature fusion and apply our framework to the fused features to complete the task. On various human movement datasets, we obtain an overall accuracy of 92 percent.