Humans are very good at recognizing faces and if computers complex patterns.
Even a passage of time doesn't affect this capability and therefore it would help
become as robust as humans in face recognition. Machine recognition of human faces
from still or video images has attracted a great deal of attention in the psychology,
image processing, pattern recognition, neural science, computer security, and
computer vision communities. Face recognition is probably one of the most non-intrusive
and user-friendly biometric authentication methods currently available; a screensaver
equipped with face recognition technology can automatically unlock the screen
whenever the authorized user approaches the computer.
Face is an important part of who we are and how people identify us. It is arguably
a person's most unique physical characteristic. While humans have had the innate
ability to recognize and distinguish different faces for millions of years, computers
are just now catching up.
Visionics, a company based in New Jersey, is one
of many developers of facial recognition technology. The twist to its particular
software, FaceIt, is that it can pick someone's face out of a crowd, extract that
face from the rest of the scene and compare it to a database full of stored images.
In order for this software to work, it has to know what a basic face looks like.
Facial recognition software is designed to pinpoint a face and measure its features.
Each face has certain distinguishable landmarks, which make up the different facial
features. These landmarks are referred to as nodal points. There are about 80
nodal points on a human face. Here are a few of the nodal points that are measured
by the software:
Distance between eyes
" Width of nose
" Depth of eye sockets
" Jaw line
nodal points are measured to create a numerical code, a string of numbers that
represents the face in a database. This code is called a faceprint. Only 14 to
22 nodal points are needed for the FaceIt software to complete the recognition
software falls into a larger group of technologies known as biometrics. Biometrics
uses biological information to verify identity. The basic idea behind biometrics
is that our bodies contain unique properties that can be used to distinguish us
from others. Besides facial recognition, biometric authentication methods also
" Fingerprint scan
" Retina scan
" Voice identification
recognition methods generally involve a series of steps that serve to capture,
analyze and compare a face to a database of stored images. The basic processes
used by the FaceIt system to capture and compare images are:
When the system is attached to a video surveillance system, the recognition software
searches the field of view of a video camera for faces. If there is a face in
the view, it is detected within a fraction of a second. A multi-scale algorithm
is used to search for faces in low resolution. The system switches to a high-resolution
search only after a head-like shape is detected.
2. Alignment - Once a face
is detected, the system determines the head's position, size and pose. A face
needs to be turned at least 35 degrees toward the camera for the system to register
3. Normalization -The image of the head is scaled and rotated so that
it can be registered and mapped into an appropriate size and pose.
is performed regardless of the head's location and distance from the camera. Light
does not impact the normalization process.
4. Representation - The system
translates the facial data into a unique code. This coding process allows for
easier comparison of the newly acquired facial data to stored facial data.
5. Matching - The newly acquired facial data is compared to the stored data and
(ideally) linked to at least one stored facial representation.
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