Survey On Face Detection and Recognition Algorithms Using Deep Learning

A facial recognition system uses a number of algorithms to recognize faces in digital photographs, identify people, and then confirm the authenticity of the acquired images by comparing them to facial images that have been saved in a database. Biometric technology is based on facial features of a person. Face detection and Recognition are major concerns in the area of biometric based systems and purposes. This process must ensure recognition accuracy and minimum processing time. Some cutting-edge techniques allow it to be retrieved more quickly in a single scan of the raw image and lie in a smaller dimensional space while effectively keeping face information. The techniques for face detection and recognition are classified on the bases of their target application. Also, the techniques are classified and analyzed on the bases of their working domain as spatial, frequency, integrated and hardware support. Face detection is a challenging topic in computer vision because the human face is a dynamic object with a great degree of diversity in its appearance. There have been many different ways put forth, from straightforward edge-based algorithms to composite high-level systems leveraging cutting-edge pattern recognition techniques. With the help of biometrics, a facial recognition system can extract facial details from a picture or video. The data faceprint stored via facial traits is compared by the face recognition software using deep learning algorithms.Among them, face detection is a very potent tool for face recognition, image database management, human computer interface, and video surveillance. Face recognition is a rapidly developing technology that has been used extensively in forensics for purposes including criminal identification, airport security, and controlled access.