# Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos

arXiv: https://arxiv.org/abs/1802.00745 · HTML: https://mlsanity.com/paper/1802.00745/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [Fairness and Bias in Multimodal AI: A Survey](https://mlsanity.com/paper/2406.19097/) (2024)
- [Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics](https://mlsanity.com/paper/2112.08068/) (2021)
- [Inferred vs traditional personality assessment: are we predicting the same thing?](https://mlsanity.com/paper/2103.09632/) (2021)
- [Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment](https://mlsanity.com/paper/2504.11515/) (2025)
- [Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews](https://mlsanity.com/paper/2110.09424/) (2021)
- [PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment](https://mlsanity.com/paper/2609.02231/) (2026)
- [Personality Detection of Applicants And Employees Using K-mode Algorithm And Ocean Model](https://mlsanity.com/paper/2212.14675/) (2022)
- [Enhancing Apparent Personality Trait Analysis with Cross-Modal Embeddings](https://mlsanity.com/paper/2405.03846/) (2024)

## Abstract (arXiv)

Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.