A Multimodal Framework for Lie Detection Using Statistical Validation and Feature Fusion

Automated lie detection is a challenging task due to the necessity of integrating of minor behavioral, vocal, and physiological indicators. This paper introduces a multimodal architecture that combines advanced feature extraction techniques with rigorous statistical validation to improve the accuracy and utility of lie detection systems across diverse range of situations. Deep learning models like CNNs, Bi-LSTMs, and GRUs are used to extract facial microexpressions, vocal prosodic shifts and body movement patterns that are used in the proposed method. These features that are unique to each modality are integrated using an adaptive weighted fusion approach and are classified using an ensemble of Support Vector Machines, Random Forests, and Gradient Boosting classifiers.The framework uses cross-validation and statistical metrics such as area under the curve (AUC), receiver operating characteristic (ROC) curves, Kolmogorov-Smirnov (KS) tests and concordance-discordance analysis to ensure comprehensive performance evaluation. Three benchmark datasets, Real Life Trial (RLT), Bag of Lies (BoL), and DOLOS, were used for experiments. The findings demonstrated consistent performance under many conditions. The fusion model achieved an AUC of 0.94 and improved accuracy by 15% relative to unimodal baselines. The findings indicate that utilizing multimodal cues significantly improves the accuracy of lie detection, even when things change in the real-world. The system doesn't require any invasive methods and can be used immediately with standard audiovisual equipment. This indicates that it can be applicable in forensic interviews, remote evaluations, security assesments and mental health evaluations. Ethical considerations, including fairness, transparency, and human oversight, are embedded within the framework design. In general, this work improves the field by giving a method for finding multimodal deception in high-stakes environments that is statistically validated, easy to understand, and can be used.