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

arXiv: https://arxiv.org/abs/2112.08068 · HTML: https://mlsanity.com/paper/2112.08068/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Automated Analysis and Prediction of Job Interview Performance](https://mlsanity.com/paper/1504.03425/) (2015)
- [Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos](https://mlsanity.com/paper/1802.00745/) (2018)
- [AVEID: Automatic Video System for Measuring Engagement In Dementia](https://mlsanity.com/paper/1712.08084/) (2017)

## Built on by (most cited first, 4 of 4)

- [Explainable Depression Detection via Head Motion Patterns](https://mlsanity.com/paper/2307.12241/) (2023)
- [MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context Understanding](https://mlsanity.com/paper/2409.06224/) (2024)
- [Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics](https://mlsanity.com/paper/2302.09817/) (2023)
- [MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings](https://mlsanity.com/paper/2309.10765/) (2023)

## Abstract (arXiv)

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.