# Deep Temporal Analysis for Non-Acted Body Affect Recognition

## TL;DR

This paper introduces a novel deep learning approach for recognizing genuine emotions from body movements using 3D skeleton data, emphasizing temporal local features, and outperforms existing methods on the UCLIC dataset.

## Contribution

It is the first to apply deep neural networks to non-acted body affect recognition, incorporating temporal local movements and a new set of features.

## Key findings

- Outperforms existing methods on UCLIC dataset
- Incorporates temporal local body movements into analysis
- Introduces new global and time-dependent features

## Abstract

Affective computing is a field of great interest in many computer vision applications, including video surveillance, behaviour analysis, and human-robot interaction. Most of the existing literature has addressed this field by analysing different sets of face features. However, in the last decade, several studies have shown how body movements can play a key role even in emotion recognition. The majority of these experiments on the body are performed by trained actors whose aim is to simulate emotional reactions. These unnatural expressions differ from the more challenging genuine emotions, thus invalidating the obtained results. In this paper, a solution for basic non-acted emotion recognition based on 3D skeleton and Deep Neural Networks (DNNs) is provided. The proposed work introduces three majors contributions. First, unlike the current state-of-the-art in non-acted body affect recognition, where only static or global body features are considered, in this work also temporal local movements performed by subjects in each frame are examined. Second, an original set of global and time-dependent features for body movement description is provided. Third, to the best of out knowledge, this is the first attempt to use deep learning methods for non-acted body affect recognition. Due to the novelty of the topic, only the UCLIC dataset is currently considered the benchmark for comparative tests. On the latter, the proposed method outperforms all the competitors.