Sayde L. King

Sayde L. King

NASEM Postdoctoral Research Associate

Cross-Domain Deception Dataset (CD3)

Description

The Cross-Domain Deception Dataset (CD3) contains frame-level visual features extracted from interview video recordings to support research in deception detection using facial expressions, action units, gaze, and body/hand gestures.

Using a commercial laptop and Microsoft Teams, 45 participants completed mock interviews across two sessions, responding to questions about biography, academic success, and well-being.

The dataset provides 1,270 truthful clips and 587 deceptive clips, enabling cross-domain analysis of how deception appears differently across content areas and supporting research into well-being–specific deception models.

Included Features

Each sample includes 983 frame-level features, including:

Gaze

  • 8 gaze features
  • Direction vectors
  • Gaze angles

Landmarks

  • 136 × 2D facial landmarks
  • 204 × 3D facial landmarks
  • 112 × 2D eye landmarks
  • 168 × 3D eye landmarks
  • 140 × 2D face keypoints

Head Pose

  • Translation: x, y, z
  • Rotation: pitch, yaw, roll

Face Shape

  • 40 PCA-based shape parameters

Facial Action Units

  • 35 total action unit features

    • 18 presence features
    • 17 intensity features

Body & Hands

  • 50 body keypoints, 2D
  • 84 hand keypoints, 2D

Labels & Identifiers

  • Deception label

    • 1 = deceptive
    • 0 = truthful

  • Participant ID

    • Format: PXXX

File Format

  • Provided in .csv format
  • Each row represents one video frame from a participant response

Suggested Uses

  • Deception detection, including cross-domain and well-being–specific modeling
  • Action unit and gaze-based behavioral modeling
  • Gesture and micro-expression analysis
  • Domain adaptation and cross-domain inference
  • Multimodal vision features for cognitive state estimation
  • Representation learning for social and behavioral computing

Educational & Research Use

This dataset is available for coursework, capstone projects, theses, and experimentation in deception detection, behavioral modeling, and multimodal machine learning.

Citation

S. L. King and T. Neal, “Exploring Vision-Based Features for Detecting Deception in Well-Being: A Cross-Domain Comparison,” 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition (FG), Tampa/Clearwater, FL, USA, 2025, pp. 1–10, doi: 10.1109/FG61629.2025.11099290.