Inspiration

Detecting Truth in Real-World Scenarios Limitations of Traditional Polygraph Systems Human Behavior & Psychological Cues Rise of Artificial Intelligence in Daily Life Real-Time Facial Analysis Technology Need for Smart Security Systems AI in Crime Investigation & Fraud Detection Combining Computer Vision and Machine Learning Automation of Human Judgment Future of Intelligent Surveillance Systems

What it does

The system observes a person while they answer questions and then:

👉 Analyzes their behavior 👉 Finds patterns linked to lying 👉 Gives a result: Truth or Lie

How we built it

Project Planning & Idea Selection Data Collection Data Preprocessing Feature Extraction (Voice & Face) Model Selection Model Training Model Evaluation Integration of Modules User Interface Development Testing & Debugging

Challenges we ran into

Lack of Quality Dataset Detecting Subtle Human Expressions Noise in Audio Data Real-Time Processing Difficulty Model Accuracy Issues Overfitting of Model Integration of Multiple Inputs (Voice + Face) Hardware Limitations Lighting and Camera Issues Ethical and Privacy Concerns

Accomplishments that we're proud of

Lack of Quality Dataset Detecting Subtle Human Expressions Noise in Audio Data Real-Time Processing Difficulty Model Accuracy Issues Overfitting of Model Integration of Multiple Inputs (Voice + Face) Hardware Limitations Lighting and Camera Issues Ethical and Privacy Concerns

What we learned

Basics of Artificial Intelligence and Machine Learning Understanding of Human Behavior and Psychology Data Collection and Preprocessing Techniques Feature Extraction from Voice and Facial Data Working with Computer Vision using OpenCV Building and Training Models using TensorFlow Model Evaluation and Accuracy Improvement Handling Real-Time Data Processing Debugging and Problem-Solving Skills Importance of Ethical Considerations in AI

What's next for AI Lie Detector

Improved Accuracy with Advanced AI Models Multimodal Detection (Voice + Face + Text + Biometrics) Real-Time Detection in Everyday Devices Integration with Wearables (Smartwatches, Sensors) Use of Deep Learning like Neural Networks Emotion and Stress Level Analysis Better Datasets for Training

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