# Artificial Intelligence can Recognize Whether a Job Applicant is Selling and/or Lying According to Facial Expressions and Head Movements Much More Correctly Than Human Interviewers

arXiv: https://arxiv.org/abs/2605.17461 · HTML: https://mlsanity.com/paper/2605.17461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 80 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 8 of 8)

- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs](https://mlsanity.com/paper/1907.06724/) (2019)
- [Deception Detection in Videos](https://mlsanity.com/paper/1712.04415/) (2017)
- [Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks](https://mlsanity.com/paper/1904.01390/) (2019)
- [A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges](https://mlsanity.com/paper/1806.05781/) (2018)
- [Face-Focused Cross-Stream Network for Deception Detection in Videos](https://mlsanity.com/paper/1812.04429/) (2018)
- [Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis & Application](https://mlsanity.com/paper/1601.04805/) (2016)
- [Investigating the Reliability of Self-report Data in the Wild: The Quest for Ground Truth](https://mlsanity.com/paper/2107.00389/) (2021)

## Abstract (arXiv)

Whether an interviewee's honest and deceptive responses can be detected by facial expression signals in videos has been debated and requires further research. We developed deep learning models enabled by computer vision to extract temporal patterns of job applicants' facial expressions and head movements to identify self-reported honest and deceptive impression management (IM) tactics from video frames in real asynchronous video interviews. A 12- to 15-minute video was recorded for each of N=121 job applicants as they answered five structured behavioral interview questions. Each applicant completed a survey to self-evaluate their trustworthiness on four IM measures. Additionally, a field experiment was conducted to compare the concurrent validity associated with self-reported IMs between our modeling approach and human interviewers. Human interviewers' performance in predicting these IM measures from another subset of 30 videos was obtained by having N=30 human interviewers evaluate three recordings. Our models explained 91% and 84% of the variance in honest and deceptive IMs, respectively, and showed stronger correlations with self-reported IM scores than human interviewers.