IMAGE PROCESSING IS NOW INCREASINGLY REFERRED TO AS DIGITAL IMAGE PROCESSING, A BRANCH OF COMPUTER KNOWLEDGE THAT DEALS WITH DIGITAL SIGNAL PROCESSING, REPRESENTING IMAGES TAKEN WITH A DIGITAL CAMERA OR SCANNED BY A SCANNER. IN THE SPECIFIC SENSE, IMAGE PROCESSING IS ANY TYPE OF SIGNAL PROCESSING THAT IS THE INPUT OF AN IMAGE. THE FACE PLAYS AN ESSENTIAL ROLE IN IDENTIFYING PEOPLE AND EXPRESSING THEIR EMOTIONS AT THE COMMUNITY LEVEL. THE HUMAN ABILITY TO RECOGNIZE FACES IS REMARKABLE. WE CAN RECOGNIZE THE THOUSANDS OF FACES TAUGHT THROUGHOUT OUR LIVES AND IDENTIFY THEM AT A GLANCE. THE CMPA FRAMEWORK APPLIES TO EXPERIMENTS THAT WERE PART OF THE FACE OF A COMPETITION IDENTIFIED. ANALYSIS SHOWS THAT ALGORITHMS ARE BETTER THAN HUMANS WITHOUT CONTRADICTION, TO MATCH FACES IN STILL IMAGES. FOR THE VIDEO AND THE DANGERS OF FACES, PEOPLE ARE SUPERIOR. FINALLY, BASED ON THE CMPA FRAMEWORK, WE HAVE DEVELOPED A FACE-TO-FACE INDEX OF A COMPETITIVE PROBLEM FOR EXPANSION ALGORITHMS THAT ARE SUPERIOR TO HUMAN BEINGS FOR FACE DETECTION PROBLEMS. HMM'S APPROACH TO MATCHING IMAGE TEMPLATES TO A SEQUENCE OF MODEL MODES STOCHASTIC IS BASED ON A DOUBLE-LAYERED STRUCTURE. THIS SECTION OUTLINES THE BASIC FOUNDATIONS OF HMM AND DESCRIBES HOW TO USE IT TO DETECT FACES. EXPLAIN THE FEATURES AND PARTITIONING OF EXERCISE DATA IN THIS MODEL. SEE THE EVALUATION AND FEATURES THAT HAVE BEEN OBTAINED. IT LOOKS LIKE EACH SECTION PROVIDES A FEATURE (NOSE, EYES, FOREHEAD, ...). THE USE OF THE HIDDEN MARKOV MODEL HAS SIGNIFICANTLY IMPROVED THE IDENTIFICATION RATE.