A deep learning approach for face detection

Face detection has become a crucial area at the intersection of Artificial Intelligence and Computer Vision. Its growing significance is due to its wide range of applications, including security and everyday interactions, which necessitate precise and reliable systems. The capability to identify and recognize individuals based on facial features offers significant potential, making it an important tool in several contexts. This paper presents a face detection model using Machine Learning techniques. The main purpose is to create a system capable of accurately and efficiently identifying and bounding faces within images. The proposed architecture pipeline includes image capture, annotation and augmentation, and use of a modified version of the VGG16 Classification Network. The results show that the developed approach performs face detection with high precision, presenting an average IoU accuracy of 89% and mAP values of 97%, 92%, and 83%, at IoU thresholds of 0.5, 0.75, and 0.95, respectively.