Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics

TL;DR

This paper introduces kinemes, elementary head-motion units, as a novel, explainable approach for predicting personality and interview traits from head motion dynamics, demonstrating effective and interpretable behavioral analytics.

Contribution

It presents a new kineme-based method combined with FACS features for trait prediction, improving interpretability and performance over traditional facial image analysis.

Findings

LSTM with kineme sequences outperforms CNN with facial images.

Combining FACS AUs with kinemes enhances prediction accuracy.

Prediction performance depends on the observation time length.

Abstract

We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits. Transforming head-motion patterns into a sequence of kinemes facilitates discovery of latent temporal signatures characterizing the targeted traits, thereby enabling both efficient and explainable trait prediction. Utilizing Kinemes and Facial Action Coding System (FACS) features to predict (a) OCEAN personality traits on the First Impressions Candidate Screening videos, and (b) Interview traits on the MIT dataset, we note that: (1) A Long-Short Term Memory (LSTM) network trained with kineme sequences performs better than or similar to a Convolutional Neural Network (CNN) trained with facial images; (2) Accurate predictions and explanations are achieved on combining FACS action units (AUs) with kinemes, and (3) Prediction performance is affected by the time-length over which head and facial movements are observed.