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arxiv: 2605.17461 · v1 · pith:Z3GWZ76Inew · submitted 2026-05-17 · 💻 cs.HC · cs.AI· cs.CY

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

Hung-Yue Suen , Kuo-En Hung , Che-Wei Liu , Yu-Sheng Su , Han-Chih Fan This is my paper

Pith reviewed 2026-05-19 22:44 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.CY
keywords impression managementdeception detectionfacial expressionshead movementsdeep learningvideo interviewscomputer visionAI performance comparison
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The pith

Deep learning models detect honest and deceptive impression management in job interviews from facial expressions and head movements more accurately than human interviewers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper trains deep learning models on videos of 121 job applicants responding to structured behavioral questions in asynchronous interviews. The models extract temporal patterns in facial expressions and head movements to match the applicants' own post-interview self-reports on four measures of honest and deceptive impression management tactics. In a side-by-side test on 30 videos, the models produced stronger correlations with those self-reports than 30 human interviewers achieved. If correct, this would mean automated analysis can supply more consistent evaluations of how candidates present themselves than human judgment alone. Readers care because hiring decisions often hinge on assessing authenticity in ways that are hard for people to do reliably.

Core claim

Deep learning models enabled by computer vision extract temporal patterns of facial expressions and head movements from video frames in real asynchronous video interviews to identify self-reported honest and deceptive impression management tactics, explaining 91% of the variance in honest IM and 84% in deceptive IM while showing stronger correlations with self-reported IM scores than human interviewers.

What carries the argument

Deep learning models using computer vision to extract temporal patterns of facial expressions and head movements from video frames for predicting impression management scores.

If this is right

  • Automated video analysis can predict honest and deceptive impression management with higher concurrent validity than human evaluators.
  • Structured behavioral interview videos contain sufficient visual signals for models to account for most of the variance in self-reported IM tactics.
  • AI approaches offer a scalable alternative for screening applicant trustworthiness in asynchronous interview formats.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Widespread use could shift hiring toward standardized visual screening and reduce differences in how individual interviewers interpret the same cues.
  • The same modeling approach might be tested on synchronous live interviews or extended to other professional interactions like sales calls.
  • Organizations would need policies for consent and data handling when applying facial analysis to employment decisions.

Load-bearing premise

Self-reported impression management measures from post-interview surveys serve as accurate and unbiased ground truth for training the models and for comparing them to human interviewers.

What would settle it

An independent study that measures actual deceptive behaviors through methods such as reference checks or follow-up performance data and finds the models do not outperform humans in predicting those verified behaviors.

Figures

Figures reproduced from arXiv: 2605.17461 by Che-Wei Liu, Han-Chih Fan, Hung-Yue Suen, Kuo-En Hung, Yu-Sheng Su.

Figure 1
Figure 1. Figure 1: Face mesh topology with facial landmarks. The MediaPipe Face Mesh utilizes two interconnected NN models to deduce the 3D surface geometry of a human face from a single smartphone or webcam input, eliminating the need for specialized depth sensors. One model processes the entire image to identify face locations, while the other focuses on these located faces to predict their 3D surface geometry using regres… view at source ↗
Figure 2
Figure 2. Figure 2: Eye corner alignment To convert the original 2D images into a vector of 3D coordinates, we transformed these images into 3D space using a Cartesian coordinate system [54], as shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: 2D to 3D image transformation We obtained P(𝑋𝑓𝑚, 𝑌𝑓𝑚, 𝑍𝑓𝑚) from FaceMesh, where the y￾coordinate matches the previously mentioned horizontal axis. The x and y coordinates of the vertices match the points in the 2D image. We rotated the 2D images along the x-, y-, and z￾axes using the Cartesian coordinate system. The height (h) and width (w) of the image were calculated as depicted in equation (1), while th… view at source ↗
Figure 4
Figure 4. Figure 4: Proposed Image Processing and 3D-CNN - LSTM Regression Models IV. RESULTS A. Self-reported IM: Statistics, Validity, and Reliability To assess the construct validity and reliability of our model for self-reported IMs — including honest self-promotion, honest [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that deep learning models using computer vision on facial expressions and head movements from 12-15 minute asynchronous video interviews (N=121 applicants answering five behavioral questions) can predict self-reported honest and deceptive impression management (IM) tactics with R²=0.91 and 0.84 respectively, and that these models show stronger correlations with the self-reported IM scores than N=30 human interviewers evaluating a subset of 30 videos.

Significance. If the self-reported IM scores validly index objective behavior, the high variance explained and outperformance of humans could support automated screening tools in hiring. The results highlight potential for temporal pattern analysis in video interviews, but significance is limited by the absence of independent validation that self-reports track actual lying or selling rather than self-perception.

major comments (3)
  1. [Abstract] Abstract: The title and abstract assert that the models recognize 'whether a job applicant is selling and/or lying' from facial/head signals, yet the models are trained and evaluated exclusively against post-interview self-reported IM scores with no independent behavioral coding, physiological measures, or third-party ratings to establish that these scores index actual deceptive tactics rather than social-desirability bias or inaccurate self-insight.
  2. [Field experiment] Field experiment section: The concurrent validity comparison shows models outperforming humans in predicting the same self-reported IM targets (N=30 videos), but this does not demonstrate superior detection of objective IM because both the AI and human judgments are benchmarked against the identical self-report criterion; superior correlation with self-reports does not entail better recognition of actual lying/selling.
  3. [Results] Results: The reported R² values of 91% and 84% on N=121 samples are presented without details on train-test splits, cross-validation procedure, or regularization to control overfitting in the deep learning models, which is load-bearing for interpreting the performance as generalizable rather than label-fitting on the self-report targets.
minor comments (2)
  1. [Abstract] Abstract and methods: No description of the specific computer vision pipeline, model architecture (e.g., CNN/RNN layers), or feature extraction for temporal facial and head movement patterns.
  2. [Discussion] The manuscript lacks discussion of prior literature on the known limitations of self-report IM measures in interview contexts or explicit acknowledgment that the ground truth is perceptual rather than behavioral.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for these insightful comments, which help clarify the boundaries of our claims. We address each major point below, acknowledging where revisions are needed to better align language with the self-report criterion used throughout the study.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The title and abstract assert that the models recognize 'whether a job applicant is selling and/or lying' from facial/head signals, yet the models are trained and evaluated exclusively against post-interview self-reported IM scores with no independent behavioral coding, physiological measures, or third-party ratings to establish that these scores index actual deceptive tactics rather than social-desirability bias or inaccurate self-insight.

    Authors: We agree that the title and abstract phrasing could be more precise. The models were developed and evaluated to predict self-reported honest and deceptive impression management scores, which serve as the criterion variable in line with prior organizational psychology research on IM tactics. We will revise the title and abstract to explicitly state that the models predict self-reported IM rather than claiming direct recognition of objective lying or selling behavior, and we will add a sentence in the discussion noting the reliance on self-reports as a limitation. revision: yes

  2. Referee: [Field experiment] Field experiment section: The concurrent validity comparison shows models outperforming humans in predicting the same self-reported IM targets (N=30 videos), but this does not demonstrate superior detection of objective IM because both the AI and human judgments are benchmarked against the identical self-report criterion; superior correlation with self-reports does not entail better recognition of actual lying/selling.

    Authors: We concur that the field experiment demonstrates superior concurrent validity with the self-reported IM scores rather than superior detection of objective behavior. The comparison is valuable for showing that the AI approach aligns more closely with applicants' own reports of their IM tactics than human interviewers do. We will revise the results and discussion sections to frame the finding explicitly as better prediction of self-reported IM and to avoid any implication of objective superiority without additional validation. revision: partial

  3. Referee: [Results] Results: The reported R² values of 91% and 84% on N=121 samples are presented without details on train-test splits, cross-validation procedure, or regularization to control overfitting in the deep learning models, which is load-bearing for interpreting the performance as generalizable rather than label-fitting on the self-report targets.

    Authors: This point is well taken. The current manuscript lacks sufficient detail on the modeling pipeline. In the revision we will add a dedicated subsection describing the train-test split strategy, cross-validation approach (including k-fold details), regularization methods, and any hyperparameter tuning used to address potential overfitting. We will also report performance on held-out test data to support the interpretability of the R² values. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is standard supervised learning on chosen labels

full rationale

The paper extracts facial and head-movement features from N=121 videos, trains models to predict the applicants' self-reported IM scores on four measures, reports R² values of 0.91/0.84, and shows higher correlation with those same self-report scores than human raters achieve on a separate set of 30 videos. This is a conventional supervised-learning pipeline whose performance metrics are defined directly against the chosen target variable. No equation reduces to its input by construction, no parameter is fitted on one subset and then presented as an independent prediction of a distinct quantity, and no self-citation or uniqueness theorem is invoked to justify the central mapping. The assumption that self-reports validly index objective lying/selling is a substantive validity claim rather than a definitional or statistical circularity; the reported derivation chain remains self-contained against the paper's own benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that facial and head movement patterns carry detectable signals of impression management, plus the untested premise that self-reports are faithful proxies; the deep learning component introduces numerous fitted parameters whose values are not reported.

free parameters (1)
  • deep learning model hyperparameters and weights
    Neural network parameters are optimized during training to maximize fit to the self-reported IM labels from the N=121 participants.
axioms (1)
  • domain assumption Facial expressions and head movements contain temporal patterns that reliably indicate honest and deceptive impression management tactics.
    Invoked to justify the computer vision extraction step as a valid input for identifying self-reported IM.

pith-pipeline@v0.9.0 · 5739 in / 1489 out tokens · 42968 ms · 2026-05-19T22:44:47.591123+00:00 · methodology

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Reference graph

Works this paper leans on

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