Multimodal Lie Detection via ML Fusion of Brain, Audio, and Video Signals
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Solution Overview
Problem
Current lie detection methods, such as polygraph tests and truth drugs, are often inaccurate and have side effects, and there is a lack of automated systems using multimodal data from audio, video, and brain signal sources for robust and accurate lie detection.
Innovation Solution
A Machine Learning (ML) model-based system that extracts features from multiple data sources, including brain signals, video, and audio, at predefined intervals, combines them to form multimodal data, and processes this data to generate labels indicating truth or lies with a confidence score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polygraph test is used for lie detection, then quantitative discrimination between lie and truth can be achieved, but false positive results occur when true statement givers are nervous
Solution Approach 1:
The patent combines multiple data sources (audio, video, brain signals) into a unified lie detection system. By merging these different modalities, the system achieves more reliable lie detection that is not susceptible to single-source errors, thereby reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The patent introduces Machine Learning models as intermediary components that process and analyze the raw data from multiple sources. These ML models serve as mediators that can distinguish between genuine stress responses and deceptive behavior, reducing false positives caused by nervousness in truthful individuals.
2Measurement precision
If truth drugs such as sodium thiopental or ethanol are used in lie detection, then lie detection capability is enhanced, but side effects on human body occur
Solution Approach 1:
The patent replaces chemical-based lie detection methods (truth drugs) with a computational system using Machine Learning models. This substitution eliminates the need for harmful substances while maintaining or improving lie detection capability through non-invasive multimodal data analysis.
Solution Approach 2:
The system uses naturally occurring physiological and behavioral signals (brain waves, voice patterns, facial expressions) that participants produce without external intervention. This self-service approach eliminates the need for administered substances, avoiding all associated side effects while capturing authentic responses.
3Device complexity
If conventional single-source lie detection methods are used, then simplicity is maintained, but robustness and accuracy of lie detection are insufficient
Solution Approach 1:
The patent merges multiple data sources (audio, video, brain signals) and processing methods into a unified lie detection system. This combination enhances robustness and accuracy by cross-validating signals across different modalities, making the system more reliable while managing complexity through integrated architecture.
Data Source
AI summary
The disclosure relates to system and method for detecting an instance of lie using a Machine Learning (ML) model. In one example, the method may include extracting a set of features from an input data received from a plurality of data sources at predefined time intervals and combining the set of features from each of the plurality of data sources to obtain a multimodal data. The method may further include processing the multimodal data through an ML model to generate a label for the multimodal data. The label is generated based on a confidence score of the ML model. The label is one of a true value that corresponds to an instance of truth or a false value that corresponds to an instance of lie.