Lie Detection Experiment Methodology: Infrared Image and Spectrum Analysis

Wen Shi Li; H. Li; S. Lu; Q. Hu

doi:10.4028/www.scientific.net/KEM.381-382.365

Paper Titles

Surface Plasmon Resonance Bio-Sensor with Full Field Phase Detection
p.349

Study on the Intravenous Lung Assist Device (ILAD) Using PZT Actuators and PVDF Sensors
p.353

Study on the Immobilization of Ni-NTA and NTA on PMMA Substrate Base
p.357

A Basic Study of the CNT-Biomolecule Conjugation by Molecular Dynamics Analysis
p.361

Lie Detection Experiment Methodology: Infrared Image and Spectrum Analysis
p.365

Study of Equilibrium Sense Improvement Displaying Visual Information
p.369

A Robotic Facial Expression Recognition System Using Real-Time Vision System
p.375

Freeform Surface Measurement from a Single Encoded Image Captured by a Camera with Varying Parameters
p.379

Development of a Flexible Temperature Sensor Array System
p.383

HomeKey Engineering MaterialsKey Engineering Materials Vols. 381-382Lie Detection Experiment Methodology: Infrared...

Lie Detection Experiment Methodology: Infrared Image and Spectrum Analysis

Abstract:

Infrared-based lie detection experiment methodology can open the human life window (0.6µm-16µm) through which the body spontaneous or active infrared responding images be gathered and analyzed by spectrum scoring in features of geography and computation. These raw images mainly load the indirect physiological, biochemical or direct brain evidences of cognitive lie and truth. Aiming at the mimic play card test, three tests of lie detection based on the medium or near infrared spectrum (NIRS) in four kinds of spectrum analysis are discussed. The instruments include the lock-in phase amplifier of Stanford University, TTM (Thermal Texture Maps) in US patent, and self-made auricular point NIRS (Chinese Jiangsu appraisal certificate No. 1017). The subjects’ signals sampling positions are on glabella, brows, abdomen or auricular point Antitragus No. 2. The spectrum analysis methods utilize the self-built feature extraction algorithms, the wavelet microscopy, and the bispetrum in validities of 92%, 100%, 80% and 80%. The results support the lie-truth law of “truth be the baseline of lie detection in view of brain power” (the statistical finding from review of 3D-MRI lie-detection reports included our work, subjects n = 4) for making foundation to the future lie detection 3D-SoC design.