Biometrics With Facial Recognition Is Now Widely Used. A Face Identification System Should Identify Not Only Someone's Faces But Also Detect Spoofing Attempts With Printed Face Or Digital Presentations. A Sincere Spoofing Prevention Approach Is To Examine Face Liveness As Eye Blinking. Nevertheless, This Approach Is Helpless When Dealing With Video-based Replay Attacks. For This Reason, This Paper Proposes A Combined Method Of Face Liveness Detection And CNN (Convolutional Neural Network) Classifier. The Test Results Show That The Module Created Can Recognize Various Kinds Of Facial Spoof Attacks, Such As Using Posters, Masks, Or Smartphones And Second Step Verification. Then Develops A Model To Classify Each Character's Face From A Captured Image Using A Collection Of Rules I.e., HOG Algorithm To Record The Student Attendance. The Proposed ASAS (Automated Smart Attendance System) Will Capture The Image And Will Be Compared To The Image Stored In The Database. ASAS Marks Individual Attendance, If They Image Matches The Image In The Database. The Proposed Algorithm Reduces Effort And Captures Day-to-day Actions Of Managing Each Student And Also Makes It Simple To Mark The Presence.
