FACIAL RECOGNITION
Facial recognition is a bio metric software application capable of
uniquely identifying or verifying a person by comparing and analyzing patterns
based on the person's facial contours.
Facial recognition is mostly used for security purposes,
though there is increasing interest in other areas of use. In fact, facial
recognition technology has received significant attention as it has potential
for a wide range of application related to law enforcement as well as other
enterprises. Facial recognition is also known as face recognition.
There are different facial recognition techniques in use,
such as the generalized matching face
detection method and the adaptive
regional blend matching method. Most facial recognition systems
function based on the different nodal points on a human face. The values
measured against the variable associated with points of a person’s face help in
uniquely identifying or verifying the person. With this technique, applications
can use data captured from faces and can accurately and quickly identify target
individuals. Facial recognition techniques are quickly evolving with new
approaches such as 3-D modeling, helping to overcome issues with existing
techniques.
STEPS IN FACE
IDENTIFICATION
DETECTION:
When the facial recognition system is attached to a video surveillance system,
the recognition software scans the field of view of the camera for what it
detects as faces. Upon the detection of each face-like image on a head-shaped
form, it sends the face to the system to process it further. The system then
estimates the head’s position, orientation, and size. Generally, a face needs
to be turned at least 35 degrees toward the camera for the camera to detect it.
NORMALIZATION:
The image of the captured face is scaled and rotated so that it can be
registered and mapped into an appropriate pose and size. This is called
normalization. After normalization, the software reads the geometry of the face
by determining key factors, include the distance between the eyes, the
thickness of the lips, the distance between the chin and the forehead, and many
others. Some advanced face recognition systems use hundreds of such factors.
The result of this processing leads to the generation of what is called a
facial signature.
REPRESENTATION:
After forming the facial signature, the system converts it into a unique code.
This coding facilitates easier computational comparison of the newly acquired
facial data to stored databases of previously recorded facial data.
MATCHING:
This is the final stage in which newly acquired facial data is compared to the
stored data; if it matches with one of the images in the database, the software
returns the details of the matched face and notifies the end user.
ADVANTAGES
·
Compared to other biometric techniques, facial
recognition is of a non-contact nature.
·
Face images can be captured from a distance and
can be analyzed without ever requiring any interaction with the user/person. As
a result, no user can successfully imitate another person.
·
Facial recognition can serve as an excellent
security measure for time tracking and attendance.
·
Facial recognition is also cheap technology as
there is less processing involved, like in other biometric techniques.
DRAWBACKS
·
Facial recognition can only identify people when
the conditions such as lighting are favorable.
·
The application could be less reliable in case
of insufficient light or if the face is partially obscured.
·
Another disadvantage is that facial recognition
is less effective when facial expressions vary.
