A Novel Face Liveness Detection System for the Identification of Real and Fake Images Using Deep Learning Model

A growing number of applications exist for the exciting and rapidly developing science of face detection. A person’s level of activity can be estimated using the liveness parameter employed in the face detection process. In the face detection method, an enormous amount of the person’s face captures and motions are recorded in order to evaluate his liveness by decreasing down to various pixel sizes of data. Artificial intelligence frequently struggles with face recognition. Our daily lives make extensive use of this application. In order to protect private information, a number of smartphones have face recognition features that open phones. These features are also used on Facebook to swiftly identify Facebook users who appear in pictures. Face recognition has been suggested in a variety of ways up to this point, but it is still quite difficult to use in practical situations. Spoofing is the practice of using false biometric features to gain unauthorized access to resources guarded by biometric authentication systems. The results show that, when employing the database’s specified assessment protocols, our solution performs better than cutting-edge methodologies.