Face Recognition

What is face recognition?

Face recognition is a method of identifying or verifying the identity of an individual using their face.

The Face Recognition technique is one of the most efficient biometric techniques for identification of people.

Face recognition systems can be used to identify people in photos, video, or in real-time.

A biometric technique which is capable of uniquely identifying or verifying a person by comparing and analyzing patterns based on the person’s facial contours.

What is Biometric?

Biometrics is the technical term for body measurements and calculations. It refers to metrics related to human characteristics.

Biometrics are physical or behavioral human characteristics to that can be used to digitally identify a person to grant access to systems, devices or data.

Used to automatically recognize an individual’s identity

A ”biometric system” refers to integrated hardware and software used to conduct biometric identification.

There are Two types:

physiological [Face-scan, Finger-scan, Hand-scan, Retina-scan, Iris-scan]
behavioral characteristics [Voice-scan, Signatue-scan, Keystroke-scan ]

History of biometric :

IN 1960s,W.BLEDSOE,H.C.WOLF AND C.BISSON INTRODUCED SEMI AUTOMATED SYSTEM
1970s,GOLDSTEIN,HARMON AND LESK
1988,KIBBY AND SIROVICH
1993 FERET WAS SPONSORED BY DARPA
FRVT WERE DONE IN 2000,2002 AND 2006
2006,FINALLY FRGC INTRODUCED NEW ALGORITHMS THAT ARE 10 TIMES FASTER.

How Face Recognition Works?

Face recognition systems use computer algorithms to pick out specific, distinctive details about a person’s face.

These details, such as distance between the eyes or shape of the chin, are then converted into a mathematical representation and compared to data on other faces collected in a face recognition database.

The data about a particular face is often called a face template and is distinct from a photograph because it’s designed to only include certain details that can be used to distinguish

one face from another.

Some face recognition systems, instead of positively identifying an unknown person, are designed to calculate a probability match score between the unknown person and specific face templates stored in the database.

These systems will offer up several potential matches, ranked in order of likelihood of correct identification, instead of just returning a single result.

Face recognition systems vary in their ability to identify people under challenging conditions such as poor lighting, low quality image resolution, and suboptimal angle of view (such as in a photograph taken from above looking down on an unknown person).

When it comes to errors, there are two key concepts to understand:

A “false negative” is when the face recognition system fails to match a person’s face to an image that is, in fact, contained in a database. In other words, the system will erroneously return zero results in response to a query.

A “false positive” is when the face recognition system does match a person’s face to an image in a database, but that match is actually incorrect. This is when a police officer submits an image of “Joe,” but the system erroneously tells the officer that the photo is of “Jack.”

When researching a face recognition system, it is important to look closely at the “false positive” rate and the “false negative” rate, since there is almost always a trade-off. For example, if you are using face recognition to unlock your phone, it is better if the system fails to identify you a few times (false negative) than it is for the system to misidentify other people as you and lets those people unlock your phone (false positive). If the result of a misidentification is that an innocent person goes to jail (like a misidentification in a mugshot database), then the system should be designed to have as few false positives as possible.