Eigenfaces and LEM Face Recognition Technique

Among the most major uses of image analysis is face recognition. Face recognition has received a lot of attention due to its wide range of commercial, security, and forensic applications, as well as the accessibility of new media technologies. Several computer vision algorithms have been created over the last few decades. However, no technique exists to date that offers a comprehensive remedy to all situations and applications that identify people may confront. The presentation discusses the various challenges encountered during photo face recognition, such as pose or view angle, illumination, occlusion, facial expression, and time delay between image acquisition, as well as the various image-based approaches. Despite the fact that a large number of systems have emerged that can achieve recognition accuracy larger than 90% under controlled circumstances, major applications in real-world conditions remain difficult. The presentation also covers two robust face recognition techniques: Line Edge Map Detection (LEM) and Eigenfaces.