Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

TL;DR

This paper introduces an audiovisual deep residual network that predicts Big Five personality traits directly from videos without feature engineering, achieving high accuracy and winning third place in a challenge.

Contribution

The novel end-to-end deep residual network enables multimodal personality trait recognition without relying on traditional visual preprocessing steps.

Findings

Achieved 0.9109 accuracy in personality prediction

Won third place in the ChaLearn First Impressions Challenge

Operates without feature engineering or facial analysis

Abstract

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.