Deep Face Recognition In Machine Learning
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Deep Learning Face Recognition
Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes, and compares patterns based on the person's facial details.
The face detection process is an essential step as it detects and locates human faces in images and videos.
The face capture process transforms analogue information (a face) into a set of digital information (data) based on the person's facial features.
The face match process verifies if two faces belong to the same person.
Use in Different Objects
Faces in Photographs
Process of Automatic Face Recognition
Face Detection Task
Face Recognition Tasks
Deep Learning for Face Recognition
Face Recognition Task
The task of face recognition is broad and can be tailored to the specific needs of a prediction problem.
For example, in the 1995 paper titled “Human and machine recognition of faces: A survey,” the authors describe three face recognition tasks:
Face Matching: Find the best match for a given face.
Face Similarity: Find faces that are most similar to a given face.
Face Transformation: Generate new faces that are similar to a given face.
Face Recognition Applications
Face recognition systems identify people by their face images. Face recognition systems establish the presence of an authorized person rather than just checking whether a valid identification (ID) or key is being used or whether the user knows the secret personal identification numbers (Pins) or passwords.
To eliminate duplicates in a nationwide voter registration system because there are cases where the same person was assigned more than one identification number. The face recognition system directly compares the face images of the voters and does not use ID numbers to differentiate one from the others. When the top two matched faces are highly similar to the query face image, manual review is required to make sure they are indeed different persons so as to eliminate duplicates.
In many of the access control applications, such as office access or computer logon, the size of the group of people that need to be recognized is relatively small. The face pictures are also caught under natural conditions, such as frontal faces and indoor illumination. The face recognition system of this application can achieve high accuracy without much cooperation from the user.
Face recognition technology is used to monitor continuously who is in front of a computer terminal. It allows the user to leave the terminal without closing files and logging out. When the user leaves for a predetermined time, a screen saver covers up the work and disables the mouse & keyboard. When the user comes back and is recognized, the screen saver clears and the previous session appears as it was left. Any other user who tries to log in without authorization is denied.
Today more than ever, security is a primary concern at airports and for airline staff offices and passengers. Airport protection systems that use face recognition technology have been implemented at many airports around the world.
In October 2001, Fresno Yosemite International (FYI) airport in California deployed Viisage's face recognition technology for airport security purposes. The system is designed to alert FYl's airport public safety officers whenever an individual matching the appearance of a known terrorist suspect enters the airport's security checkpoint. Anyone recognized by the system would have further investigative processes by public safety officers. Computer security has also seen the application of face recognition technology. To prevent someone else from changing files or transacting with others when the authorized individual leaves the computer terminal for a short time, users are continuously authenticated, checking that the individual in front of the computer screen or at a user is the same authorized person who logged in.