siliconchip.com.au Australia’s electronics magazine April 2019 19Fig.10: how OpenBR works. It is an open-source biometric
software framework for facial recognition (http://
openbiometrics.org/) OpenBR can use a variety of different
facial recognition algorithms such as PCA (principal
component analysis), LBP (local binary patterns), SVM
(support vector machines), LDA (linear discriminant
analysis), HOG (histogram of oriented gradients) and more.
glasses, scarves or other objects disguising part of the
face or surrounds.Statistical facial recognition techniques
The principal component analysis and eigenfaces tech-
nique developed by Turk, Pentland, Sirovich and Kirby
mentioned above is still in use today in facial recognition
systems.
But many other techniques have now also been devel-
oped. The eigenface approach has an accuracy of about
90% with frontal face images, assuming good lighting and
an appropriate pose, but is very sensitive to those factors.
There are two main approaches to facial recognition.
These are so-called template-based methods and geomet-
ric-feature based methods.
Template-based methods utilise the whole face and
extract features from the full face image, which are then
matched to an existing face in a database using a pattern
classifier algorithm.
Geometric-feature methods locate specific landmarks on
the face such as the location of the eyes, nose, chin etc and
determine the geometric relationship between them, or al-
ternatively and more recently, match a three-dimensional
image of a face to a stored representation.
Template matching techniques require an image or a set
of images of a person’s face. The facial features are then
extracted via a mathematical process and a unique “tem-
plate” for that face is produced.
With the eigenfaces described above, this can result in
as little as 2-3kB of data per face.
This allows vast numbers of templates to be searched
in short amounts of time to find matching faces, at rates of
perhaps 100,000 faces per second. So searching a database
of all Australian residents for a match could take less than
300 seconds with a modest computer system.
Template-based methods can be divided into the follow-
ing categories: statistical, neural network, hybrid methods
(which incorporate both) and other methods. Statistical
methods are the most common.
Of those statistical methods, PCA and Linear Discrimi-
nant Analysis are very popular. Other statistical tools in-
clude Independent Component Analysis , Support Vector
Machines and kernel methods for PCA and LDA.Using DNA evidence to reconstruct an unknown face
In theory, it is possible to use traces of a criminal suspect’s
DNA to reconstruct an image of their face. Already it is possible
to determine eye, skin and hair colour from DNA but in the future,
DNA phenotyping is said to be able to predict the appearance of a
face. A website at which users can predict eye, skin and hair col-
our from a DNA sequence is at: https://hirisplex.erasmusmc.nl/Geometric techniques
Of the geometric methods, elastic bunch graph match-
ing is a common method. PCA was also developed into
Local Feature Analysis.
Fisherfaces (Fig.5) are used with the LDA statistical tech-
nique and they are similar to the eigenfaces used with PCA.
LDA is less sensitive to lighting variation and facial ex-
pressions than PCA and is said to be more accurate over-
all, but it is computationally more intensive (ie, searching
a similarly sized database takes longer).
EGBM works similarly to the processes that occur in the
human brain when recognising a face. To create a facial
model for the database, facial landmarks are determined and
nodes are created at these points and joined to one another.
The result is a graph, akin to a spider’s web, over the face.
Landmarks might include points such as the centre of
the eyes, tip of the nose, chin etc. This process is usually
carried out with images of multiple different poses.
To work well, this method requires facial landmarks to
be accurately located, a process that can be assisted by the
use of PCA and LDA methods. When it is required to iden-
tify an unknown face, the database is searched for the most
similar geometric model.
Three dimensional (3D) facial recognition is another ex-
ample of a geometric facial recognition method (see Fig.6).
This method records a three-dimensional scan of a subject’s
face (known as a “faceprint”; see Fig.7) and uses that to
make an identification.
It has the advantage that, because it is comparing 3D
shapes instead of 2D images, there are no problems that
arise from uneven lighting, differing facial orientation, fa-
cial expression, makeup etc. 3D images of a face can alsoFig.11: OpenCV is an open-source software library for
computer vision which includes the ability to perform
facial recognition (https://opencv.org/). In this example,
facial landmark detection is being used with two different
techniques. On the left, it is using Dlib and on the right, CLM-
framework. The blue lines represent the direction of gaze of
the face, which it also detects. See the video titled “Facial
Landmark Detection” at siliconchip.com.au/link/aaod for
more information.