A facial recognition system is an information processing
system application for automatically identifying or validating a person from a
digital image or a video frame from a video source. One of the ways to do this is
by comparing selected facial features from the image and a facial database.
It is typically used in security systems and can be compared
to other biometrics such as fingerprint or eye iris recognition systems.
In recent years automatic face recognition has received significant
attention from both research communities and the securities industry, but still
remained very difficult in real applications. A number of typical algorithms are presented, being classified
into appearance-based and model-based schemes.
Classification
of Face Recognition Scenarios:
Face recognition scenarios can be classified into two types,
(i) Face verification (or authentication)
(ii) Face identification (or recognition).
Face verification is a ”Am I who I say I am” based model.
It is a one-to-one match that compares a picture with a previously stored one. To
evaluate the verification performance, the verification rate vs. false accept rate
is plotted, This is called ROC curve. A good verification system should balance
these two rates based on operational needs.
Face identification is a ”Who am I” based model. It is a
one-to-many matching process that compares a query face image against all the template
images in a face database to determine the identity of the query face . The identification
of the test image is done by locating the image in the database who has the highest
similarity with the test image.
Recognition algorithms can be divided into two main approaches,
geometric, which deals with distinguishing features, or photometric, which distils
an image into values and compares the values with templates to eliminate variances.
3-dimensional recognition:
A newly emerging trend, claimed to achieve improved accuracies,
is three-dimensional face recognition. This technique uses 3D sensors to capture
information about the shape of a face. This information is then used to identify
peculiarities on the surface of a face.
One advantage of 3D face recognition is that it is not affected
by changes in lighting like other techniques. It can also identify a face from a
range of viewing angles. 3D data points from a face immensely improve the preciseness
of facial recognition. 3D research is enhanced by the development of sophisticated
sensors that do a better job of capturing 3D face imagery. The sensors work by projecting
structured light onto the face. Each image sensor captures a different part of
the spectrum.
Skin texture analysis:
Another emerging trend uses the visual details of the skin,
as captured in standard digital or scanned images. This technique, called skin
texture analysis, turns the unique lines, patterns, and spots apparent in a person’s
skin into a mathematical space.
Tests have shown that with the addition of skin texture analysis,
performance in recognizing faces can increase 20 to 25 percentage.
