Manual Testing: Conducted manual testing to evaluate the accuracy and reliability of the facial recognition algorithm under various lighting conditions, angles, and facial expressions. Verified user identity matching and false acceptance/rejection rates.

Automated Testing: Employed automated scripts to simulate different facial recognition scenarios, ensuring the system’s consistent performance. Automated tests covered edge cases such as partial obstructions, low-resolution images, and multiple faces in a single frame.

Bug Tracking Sheets: Maintained detailed bug tracking sheets to log issues related to image processing, algorithm accuracy, and integration with other modules. Recorded the severity of each bug, steps to reproduce, and status updates throughout the testing lifecycle.

Test Cases Documentation: Developed a set of test cases focusing on different use cases, including user enrollment, face matching, liveness detection, and system alerts. Documented the expected versus actual results to measure system performance.

Performance Testing: Conducted load testing to measure the system’s response time and recognition speed under high volumes of image data. Assessed the impact of multiple concurrent users on the system’s performance.

Data Privacy and Security Assessment: Evaluated the system’s data handling processes to ensure compliance with privacy regulations. Conducted tests to confirm the secure storage and transmission of facial recognition data.

User Feedback Incorporation: Collected feedback from real users during pilot testing phases, focusing on user experience and system accuracy. Made iterative improvements to the algorithm based on this feedback.

Reporting and Analysis: Produced detailed test reports to summarize findings, including recognition accuracy metrics, bug statistics, and system performance trends. These reports helped guide further development and optimization efforts.

Manual Testing: Performed manual testing to evaluate the system’s ability to accurately detect and recognize vehicle number plates in various conditions, such as different lighting, weather, and motion. Verified the system’s recognition accuracy for plates with different fonts, sizes, and layouts.

Automated Testing: Utilized automated testing scripts to simulate a range of scenarios, including high-speed vehicle movement, occluded plates, and different plate angles. Automated tests helped ensure the system’s consistency and robustness in real-world conditions.

Bug Tracking Sheets: Created detailed bug tracking sheets to document any issues found during testing, such as misrecognition, false positives/negatives, and integration problems. Tracked the severity, reproduction steps, and resolution status for each bug.

Test Cases Documentation: Developed comprehensive test cases to cover different aspects of the system, including image capture, plate detection, optical character recognition (OCR), and database matching. Compared the expected and actual results for each test case to validate the system’s accuracy.

Performance Testing: Conducted load testing to evaluate the system’s performance under various traffic densities and vehicle speeds. Assessed the impact of processing multiple images simultaneously on system response times and accuracy.

Integration Testing: Tested the integration of the ANPR system with other systems, such as databases, traffic management, and law enforcement tools. Ensured seamless data exchange and accurate alerts for detected violations.

Environmental Condition Testing: Evaluated the system’s performance in different environmental conditions, such as low light, rain, fog, and glare. Analyzed the effects of these factors on plate recognition accuracy and made necessary adjustments.

User Feedback Incorporation: Collected user feedback during field testing to identify practical issues in real-time use. Improved the system based on suggestions regarding user interface, alert accuracy, and operational efficiency.

Reporting and Analysis: Generated test reports summarizing the accuracy rates, system performance metrics, bug trends, and overall stability. These reports provided insights for further system enhancements and fine-tuning.