Welcome to the Human Face Detection in Low Light Conditions project! This repository focuses on enhancing the accuracy and reliability of face detection algorithms under challenging low-light environments. Detecting human faces in poor lighting is a crucial task in various applications, from security systems to mobile photography, where standard face detection methods often struggle.
Traditional face detection algorithms often falter when applied to images captured in low light. This project aims to overcome these limitations by developing and implementing techniques that improve the robustness of face detection systems in dim environments. Our approach combines state-of-the-art image preprocessing methods with advanced machine learning models to ensure that human faces can be accurately detected even under suboptimal lighting conditions.
- Low-Light Image Enhancement: Techniques to improve image brightness and contrast, making faces more detectable.
- Advanced Detection Algorithms: Custom models fine-tuned for low-light scenarios, reducing false positives and improving detection rates.
- Dataset and Benchmarking: A comprehensive dataset with labeled low-light images, used for training and evaluation of models.
- Open-Source and Extensible: The project is open for contributions and designed to be easily integrated with other face detection systems.
To get started with this project, reach out to me on github.com/Futuredhruv