Problem Statement:

Traditional polygraph tests have been the standard for lie detection, but they are often criticized for their lack of accuracy and reliability. This project aimed to develop a more accurate and reliable lie detection system using micro-expression analysis. Micro-expressions are brief, involuntary facial expressions that reveal genuine emotions. By analyzing these micro-expressions, the system can determine the veracity of a person's statements.

Project Description:

The lie detector project involved using facial recognition technology to analyze micro-expressions and determine whether a person is lying. The system was designed to replace the traditional polygraph by providing a more scientific and data-driven approach to lie detection. It used a threshold of micro-expressions that could be associated with deceit to make its determinations.

Role and Responsibilities:

• Conducted research on micro-expressions and their association with deceit.

• Developed the facial recognition algorithm to detect and analyze micro-expressions.

• Implemented the system to classify statements as truthful or deceitful based on micro-expression analysis.

• Evaluated the system's accuracy and effectiveness through testing and validation.

• Documented the development process and results.

Process and Methodologies:

• Research and Literature Review:

• Conducted a thorough literature review on micro-expressions and their correlation with lying.

• Identified key facial expressions that are commonly associated with deceit.

• Data Collection and Preprocessing:

• Collected a dataset of facial expressions, including micro-expressions associated with various emotions.

• Preprocessed the data by annotating and normalizing facial images for accurate detection.

Algorithm Development:

• Developed the facial recognition algorithm using tools such as OpenCV for image processing and TensorFlow for building machine learning models.

• Implemented Convolutional Neural Networks (CNNs) to detect and analyze micro-expressions.

• Created a threshold model to classify statements based on the frequency and intensity of detected micro-expressions.

System Implementation and Testing:

• Implemented the lie detector system and conducted extensive testing to evaluate its accuracy.

• Compared the system's performance with traditional polygraph tests to validate its effectiveness.

Optimization and Refinement:

• Fine-tuned the algorithm to improve detection accuracy and reduce false positives.

• Implemented additional features to enhance the system's usability and reliability.

Challenges and Solutions:

Challenge: Accurate Detection of Micro-Expressions

Solution: Used advanced facial recognition algorithms and high-quality training datasets to improve detection accuracy.

Challenge: Reducing False Positives

Solution: Implemented a multi-stage classification approach and fine-tuned the threshold model to minimize false positives.

Key Learnings and Skills:

• Gained expertise in facial recognition technology, specifically using OpenCV for image processing and TensorFlow for machine learning model development.

• Developed skills in micro-expression analysis and the implementation of Convolutional Neural Networks (CNNs).

• Enhanced abilities in data preprocessing, system implementation, and testing using Python.

• Acquired knowledge in the ethical considerations and practical applications of lie detection technology.

Results and Impact:

• Successfully developed a lie detection system that uses micro-expression analysis to determine the veracity of statements.

• The system demonstrated higher accuracy and reliability compared to traditional polygraph tests.

• Provided a scientific and data-driven approach to lie detection, contributing valuable insights to the field of forensic psychology and criminal justice.

Visuals:

• System Architecture Diagrams: Illustrating the components of the lie detection system.

• Sample Outputs: Screenshots of detected micro-expressions and classification results.

• Performance Metrics: Graphs showing the system's accuracy, precision, and recall compared to polygraph tests.