A critique report on a developed face recognition algorithm 2022

Algorithm development critique
Title
A critique report on a developed face recognition algorithm
Table of Contents
Title............................................................................................................................ 1
Background............................................................................................................. 2
Introduction............................................................................................................. 2
Content................................................................................................................... 3
Innovation............................................................................................................... 4
Technical quality..................................................................................................... 5
X factor.................................................................................................................... 5
Presentation............................................................................................................ 7
List of reference...................................................................................................... 8
1

Algorithm development critique
Background
The paper titled Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
by James and David is chosen for reading since the idea in the paper presented clearly. The
development of an algorithm capable of identifying and authenticating facial images is important
and unique in the science field. The major contribution the paper has made is to develop an
algorithm that is insensitive to light, pose and glass differences and also it has laid a strong
scientific background to the development of other facial recognition algorithm.
Introduction
Technology changes every day. The upgrades in this field of technology are undeniable and
amusing as they consistently produce new innovations and creativity into the field. According to
Chen B 2012 the face recognition system is been used in my many fields of technology
especially in mobile phones industries. Nearly all android phones been produced recently have
the face recognition system install as a way of unlocking the mobile device. According to
Wechsler H 2012 the face recognition knowledge has moved to all dimension of technology and
science fields such as in hospitals, airports and boundary checks, advertisement and also in sports
to verify the identity of the individuals included.
According to Gallagher A.C 2012 the images taken by the face algorithm devices have
improved user interface enabling them to have advanced features including Passive UX (no
activity required), single shot analysis, availability of cross channel inputs, detection of hard
design images (flat images) and also the ability to authenticate natural face activities which are
aimed at improving the securities of the devices having this application. Zhang X and Gao Y
2012 suggests that face recognition is easily accepted by people because it is easy to use and
time saving for instance it just takes a fraction of a minute to get authenticated by the face
recognition algorithms.
Johnston R.A 2009 defined face recognition system as a technical software capable of
identifying a person or a verifying the person’s identity using facial images in order to grant
access to a facility, gadget or to pass a designated security checkpoint. According to Naseem. I,
Togneri. R and Bennamoun. M. 2010 face recognition eigen values matrices can be evaluated by
the use of principal component analysis and the linear discriminant analysis. Various innovations
have been made to improve the ability of these software facial images recognition and
verification in order to increase its security reliance. The innovative attempts includes tasting the
ability of software’s recognition and verification of facial images in different environments
including dark and bright light, detection of like faces and in different weather conditions.
The innovations include the use of both eigenfaces and fisherfaces. Ozen. F 2012 defined
eigenfaces as the values attached to computerized eigen vectors values to represent a person’s
facial image. The facial images are taken such that the probability distribution of the eigen values
are used to form the required covariance matrix by use of principal component analysis. Abidin.
Z. and Harjoko A 2012 defined fisherface as one of the most used algorithms in face recognition
2

Algorithm development critique
which was developed to determine the matrix that increases the ratio between class scatter to the
within class scatter images using elastic map matching on the linear discriminant analysis.
Content
The paper is about development of a face recognition algorithm that is not sensitive to different
variations including pose, glass recognition, light brightness variation and face expression
differences by comparing two methods based on Lambertian surface (the fisherface and
eigenface). Johnson M.K. and Adelson 2009 defined a Lambertian surface as a surface that
reflects light images with the same brightness despite the viewers or the observers viewing angle.
The design used took two approaches using a Lambertian surface. The approaches were:
i. Every image of a Lambertian surface sourced from a constant viewpoint
and illuminated differently lies in a 3D linear subspace. This is because
these Lambertian surfaces have the same light reflectance properties at all
angles.
ii. Face images are not Lambertian materials since they do form their own self
shadows and therefore not all regions of the face will have the same
Lambertian properties.
The paper also gives some insight information about the development of the facial recognition
systems siting that it begun around eighty years ago. R. Fisher designed a face recognition
pattern which uses linear discriminant analysis to verify facial images which was later named
after him to be Fisher linear discriminant analysis. He was motivated by the idea that each
human face has some different and unique facial details, just like in human being recognizing
each other through face looking. Fisher wanted the computer to do the same. His innovation has
today been used and applied almost all fields that uses computer facial vision and facial imagery
identification, also different scholars and scientist have benefitted from his work and developed
other systems related to facial recognition. Eigen face algorithm uses principal component
analysis (mostly used in pattern recognition, where by its objective is to replace the corrected
vectors dimensionally high with the uncorrected vector dimensionally low. It is effective since it
does not use a bigger storage) to compute the face authentication and face identification while
fisher uses linear discriminant analysis to compute the face authentication and face identification.
The major challenge the paper tries to solve is face authentication and face identification.
Authentication is the process of showing that something is true or correct however Fujiwara K.
2011 defined face authentication as the process of granting persons access to something or
somewhere depending on who they are. Guillaumin M. Verbeek and schmid 2009 defined face
identification as the process of recognizing and identifying persons based on facial details. The
paper attempts to test different aspects and variations of facial image recognition in order to
improve face authentication and face identification process. The variations; includes pose,
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