Quantum Sparrow Swarm Optimization with Deep Learning Enabled Deception Detection on Facial Micro Expressions

Khadija Ben Othman

Umm Al-Qura Univerity, Mekka, Saudi Arabia

Abstract

Deception detection means finding whether an individual is lying or being deceptive depending on cognitive cues, and various behavioural, or physiological. It is a significant domain of research with applications in social psychology, law enforcement, and security. Deception detection relevant to microexpressions includes examining these subtle facial cues for determining whether an individual is being deceptive or lying. Microexpressions can deliver significant cues to detect deception. Deep learning (DL) and Machine learning (ML) models were utilized for finding micro-expressions and are trained for differentiating deceptive statements from genuine ones. Still, it necessitates a diverse and large dataset of video recordings in addition to careful tuning and pre-processing of the DL approach. So, this article presents an Automated Deception Detection on Facial Microexpressions using Improved Sparrow Swarm Optimization with Deep Learning (ADDFM-ISSODL) method. The proposed ADDFM-ISSODL algorithm examines facial micro-expressions effectively for detection of deceptive behaviour. To complete this, developed ADDFM-ISSODL model uses a Gaussian filtering (GF) approach for pre-processing. Besides, ADDFM-ISSODL technique employs MobileNetv3 model for feature extraction and the hyper parameter tuning procedure performed using ISSO algorithm. The ISSO approach was designed by the integration of the standard SSO approach with the quantum evolutionary algorithm (QEA). For deception detection, a probabilistic neural network (PNN) classifier was employed. At last, grasshopper optimization algorithm (GOA) was implemented for parameter tuning of PNN method. The performance validation of ADDFM-ISSODL system tested utilizing facial expression dataset. The simulation outcome stated the greater results of ADDFM-ISSODL algorithm over other methodologies.

Keywords: Deception detection; Quantum Computing Facial microexpressions; Deep learning; Computer vision; Parameter optimization