Face Detection Using SURF Algorithm

Image Processing offers solutions to a broad range of real-world challenges. Security issues and theft have been on the rise for several decades. There has consistently been an absence of adequate security systems to ensure safety for both commercial and residential properties. Consequently, real-time surveillance has become essential. However, this necessitates high-resolution cameras and extensive storage systems to record and access the footage of the captured videos. In this study, an effort has been made utilizing a digital image processing approach that incorporates motion detection and face recognition techniques to minimize memory storage without compromising the integrity of the original image. This system aims to achieve surveillance without relying on high-end components and devices. The work is divided into three primary components: motion detection, face detection and ultimately face recognition. The reliability and efficiency of the system can be enhanced by improving its accuracy and speed. This system can be utilized by consumer markets for the surveillance of their properties. The industrial sector can adopt this method to bolster security and to ascertain whether the detected individual is an employee. This approach can be applied in apartments, home automation systems, R&D test units, restaurants and various other commercial environments.

Video Processing, Feature extraction, SURF algorithm, Face recognition, Surveillance, MTCNN

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