GetFace: A Real-time Face Detection Solution for Surveillance Footage

Abstract

Recently, facial recognition technology has advanced significantly and become widely used. However, detecting faces from surveillance camera footage in real time can be a difficult task due to a variety of factors. One major challenge is that the quality of the footage may be poor, with low resolution or high levels of noise. Additionally, lighting conditions can also make it difficult to detect faces, especially in low light environments. Furthermore, faces may be partially obscured by objects such as hats or sunglasses, or by changes in pose or expression. Finally, the number of faces in a given scene can also make it difficult to detect and track individuals in real-time. time is the presence of occlusions. Occlusions occur when an object or person blocks the view of a face, making it difficult or impossible to detect. Additionally, faces may be captured at different angles and scales, which can further complicate the detection process. Another factor that makes face detection difficult is that faces can appear differently due to various factors such as age, gender, race, and facial expressions. This can make it hard for the algorithm to generalize and detect all types of faces. In this article, we propose GetFace, an intelligent system for enhancing surveillance footage. GetFace uses CNNs, super-resolution methods, human shape and face detection techniques, Wiener filter, and face clustering to improve the image quality.