Recurrent Exposure Generation for Low-Light Face Detection

This Project introduces an innovative approach to low-light face detection, Exploiting Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and the Zero-Reference Deep Curve Estimation (Zero-DCE) algorithm. The primary goal is to improve the accuracy and reliability of face detection in challenging low-light conditions. Our method integrates LSTM and CNN networks with real-time exposure control, enabling adaptation to dynamic lighting conditions by capturing multiple frames iteratively with varying exposure levels. The incorporation of Zero-DCE facilitates the enhancement of exposure settings, resulting in improved face visibility and noise reduction. Experimental evaluations validate the efficiency of our approach, demonstrating significant advancements in low-light face detection accuracy compared to traditional methods. This project offers a practical adaptable solution with wide-ranging implications for real- world applications, including surveillance, security, and various other domains. Keywords – Low-light face detection, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), Zero- Reference Deep Curve Estimation (Zero-DCE), recurrent exposure generation, real-time exposure control, dynamic lighting conditions, noise reduction, surveillance.