An AI-powered system to enhance lie detection accuracy by analyzing facial movements, reducing human bias, and improving reliability in deception detection.
Our solution leverages Google Developer Technologies (TensorFlow, MediaPipe, Google Cloud) to create an AI-based lie detection system that integrates:
- Facial microexpression analysis using deep learning
- Body language detection via computer vision
- Voice stress & speech pattern recognition with NLP models
- This system provides a real-time probability score for deception, reducing human bias and increasing reliability in various applications, from law enforcement to corporate risk assessment.
- How different is it from any of the other existing ideas?
Our solution goes beyond traditional polygraphs by using AI-powered multimodal analysis, combining facial expressions, voice stress, and body movement detection for a more objective and accurate lie detection system.. - How will it be able to solve the problem?
By integrating machine learning and real-time behavioral analysis, the system reduces human bias, enhances reliability, and provides instant deception probability scores, making it scalable across industries like law enforcement, corporate security, and fraud prevention. - USP of the proposed solution?
- AI-driven accuracy – Reduces subjectivity and improves deception detection.
- Multimodal approach – Uses multiple behavioral cues instead of relying on a single metric.
- Scalable & real-time – Works across various sectors with instant results.
- Ethical & fair AI – Designed to ensure bias-free and responsible implementation.
- AI-Powered Facial Movement Analysis – Detects microexpressions and involuntary cues linked to deception.
- Voice Stress Detection – Analyzes pitch, tone, and hesitation patterns to identify stress indicators.
- Eye-Tracking Technology – Monitors pupil dilation, blink rate, and gaze shifts for deception cues.
- Real-Time Data Processing – Provides instant results through machine learning models.
- Bias Reduction & Ethical AI – Uses diverse datasets to improve fairness and minimize false positives.
- Scalable & Multi-Industry Use – Applicable in law enforcement, corporate security, immigration, and online fraud detection.
- User-Friendly Dashboard – Displays deception probability with detailed analysis and reporting.
- Cloud Integration – Enables remote processing and real-time accessibility.
Team leader name: Narayan U
Team members: Joyal Shibu
Razin Moyi A N K
Rudain Shiyas