Deep Learning-Powered Face Detection and Recognition for Challenging Environments

The increasing prevalence of surveillance systems in both public and private domains underscores the growing need for robust human face detection and recognition capabilities. This research introduces an innovative real-time framework that leverages deep learning techniques, particularly Convolutional Neural Networks (CNNs), to accurately detect human faces in complex images. Through a combination of image preprocessing, training using diverse dataset and classification using trained model, this system excels at identifying faces even in challenging scenarios marked by low lighting and diverse angles. This capability enables the precise identification of individuals of interest. Rigorous testing on publicly available dataset showcases the system's outstanding performance in terms of face detection and recognition accuracy. To validate the outstanding performance of CNN is face detection, comparison was done using other machine learning algorithms like k-NN, random forest and logistic regression. This research contributes to the advancement of facial recognition technology, offering a reliable solution for video surveillance applications. It holds the potential to enhance security, surveillance, and forensic practices, benefiting stakeholders ranging from law enforcement agencies to public safety organizations and private security firms.