Human Face Recognition using Wavelet Transform based
Approach
Mr. Jageshvar K. Keche
Department of Computer
Science, SSESA, Science
College, Congress Nagar,
Nagpur, (MS) - India
jkkeche@gmail.com
Mr. Vikas K. Yeotikar
Dr. Mahendra P. Dhore
Department of Computer
Science, SSESA, Science
College, Congress Nagar,
Nagpur, (MS) - India
Associate Professor
Department of Electronics &
Computer Science, RTM Nagpur
University, Nagpur, (MS) - India
vkyeotikar@gmail.com
mpdhore@rediffmail.com
ABSTRACT:
Human face recognition is the most important
research area of computer vision and pattern
recognition. This area consists of several rules
like image processing, neural networks, pattern
matching and recognition. The performance of
face recognition system is depends on its feature
extraction by using different techniques with
classification. This paper presents face
recognition using Wavelet Transform and its sub
techniques. Wavelet Transform such as Gabor
wavelet, One, Two and Three level
Decomposition approach of WT on ORL Face
database is implemented. And lastly conclude
the best recognition of human face.
Keywords:
Face
Recognition,
Pattern
Recognition, Gabor Wavelet, Wavelet Transform,
ORL.
1. INTRODUCTION
Human face recognition has become the
important area of research in computer vision,
pattern recognition and one of the most
successful applications of image analysis and
processing. It has lot of attention to the
researchers in recent years. Face recognition is
considered to be an important part of the
biometrics technique, and meaningful in
scientific research [1]. It has the potential of
being a non-intrusive form of biometric
identification.
Face recognition task is actively being used at
airports, employee entries, criminal detection
systems, etc. classifier and they shows very good
performance. The challenges of face
recognition lie in the inherent variability
arising from face characteristics like age and
gender, geometry like distance and viewpoint,
image quality like resolution, illumination,
and signal to noise ratio, and image content
like background, occlusion and disguise.
It is the ability to establish a subject’s identity
based on his facial characteristics. Automatic
face recognition has been extensively studied
over the past two decades due to its important
role in a number of application domains, such
as access control, visual surveillance [2].
Face recognition algorithms consists of two
parts: a) Face localization & normalization b)
Face identification. Dimensional reduction
techniques are used in reducing complexity of
the recognition process such as Principal
Component Analysis (PCA) [3][4] have now
been successfully applied to this problem.
Neural Network Models (NMM) [5]
technique has been applied with a training set
of images along with correct classification
that system to determine which areas of an
image are most important. Discrete Wavelet
Transform (DWT) [6], [7] has also been used
within the field of face recognition. The
popularity lies in its complete theoretical
framework, the great flexibility for choosing
bases and the low computational complexity.
Wavelet Transform is a signal analysis
method of the time scale and has a advantage
of multi-resolution analysis. It is the timefrequency localization analysis which has the
capacity of local features in the time domain
and frequency domain. Wavelet decomposition is
a multilevel dimension reduction process that
makes time–space–frequency analysis. Unlike
Fourier trans-form, which provides only
frequency analysis of signals, wavelet transforms
provide time–frequency analysis, which is
particularly useful for pattern recognition.
In this paper, we studied and presented face
recognition using Wavelet Transform techniques.
The rest of this paper is organized as follows.
Section II extends the feature mapping, which
also introduces and discusses the Wavelet
Transform and its sub-techniques in detail. In
Section III, shows experiments on ORL face
database. Finally, conclusions are drawn in
Section IV with some discussions.
2. PATTERN RECOGNITION
Pattern recognition is a field within the area of
machine learning. Alternatively, it can be defined
as “the act of taking in raw data and taking an
action based on the category of the data”. It is a
collection of methods for supervised learning.
Pattern recognition aims to classify data or
patterns based on either a prior knowledge or on
statistical information extracted from the patterns.
The patterns to be classified are usually groups of
measurements or observations that define points
in an appropriate multidimensional space. The
classification is usually based on the availability
of a set of patterns that have already been
classified or described. This set of patterns is
termed the training set and the resulting learning
strategy is characterized as supervised learning.
From the past 20 to 30 years, the growth of
pattern recognition increases day by day. The
applications of pattern recognition includes:
identification of human faces and fingerprints;
automatic recognition; character recognition;
remote sensing; medical diagnosis; biomedical
signal and image analysis; and many others.
Pattern recognition methods are classified into
two categories: structural methods and feature
space methods. The structural methods are useful
in situation where the different classes of entity
can be distinguished from each other by structural
information. For e.g. the different letters of the
alphabet are structurally different from each
other in character recognition. The earliestdeveloped structural methods based on formal
grammars to describe the structure of an
entity. The machine vision method may be
structural and based on point distribution
models, active contours, etc [8]. In featurespace methods, a set of measurements
(typically numerical) is made on each realworld entity (pattern), and from the
measurement set there is extracted a set of
features which together characterize the class
of patterns to which the given pattern belongs.
The feature-space pattern recognition is the
statistical approach, where the boundaries
between the regions representing pattern
classes in feature space are found by statistical
inference based on a design set of sample
patterns of known class members [8]. The
traditional goal of feature extraction is to
characterize the object to be recognized by
measurements whose values are very similar
for objects in the same category and very
different for objects in different categories.
This idea of seeking distinguishing features
that are invariant to irrelevant transformations
of the input. The task of the classifier
component proper of a full system is to use
the feature vector provided by the feature
extractor to assign the object to a category [9].
Image classification is implemented by
computing the similarity score between a
target feature vector and a query feature
vector [10].
2.1. Gabor Wavelet
Gabor wavelet captures the properties of
orientation selectivity, spatial localization and
optimally localized in the space and frequency
domains. It has been extensively and
successfully used in face recognition [12].
The 2D Gabor wavelet representation
pioneered by Daugman in computer vision in
1980’s [11]. The characteristics of Gabor
wavelets are quite similar to those of human
visual system for frequency and orientation
representations. This Gabor-wavelet based
extraction of features directly from the graylevel images is successful and widely been
applied to texture segmentation and fingerprint
recognition. The commonly used 2-D Gabor
filters in face recognition area [12-13] are defined
as in Eq. (1)
Where, 𝑓𝑢 and 𝜃𝑣 defines the orientation and
scale of the Gabor wavelets, 𝑓𝑚𝑎𝑥 is the
maximum central frequency and √2 is the
spacing factor between different central
frequencies. Fig.1 shows a family of Gabor
wavelets.
…(1)
Where, f is the frequency of the modulating
sinusoidal plane wave and 𝜃 is the
orientation of the major axis of the elliptical
Gaussian.
The modified Gabor wavelet is defined as:
..(2)
Where, K is an offset parameter dependent on 𝛾
and 𝜂.
The Gabor wavelet is used as the discrete wavelet
transform with either continuous or discrete input
signal. There is an intrinsic disadvantage in the
constraints 1-D and 2-D Gabor wavelets. When
extracting the features for pattern recognition,
retrieval, or computer vision purpose, the
transformed coefficients are used for distance
measure or compressed representation but not for
reconstruction, so the orthogonal constraint could
be omitted. There are some other equivalent
definitions of the Gabor wavelets and it is
defined as:
…(3)
Where, 𝑘 =2𝜋𝑓𝑒𝑥𝑝(𝑗𝜃), and the scaling functions
for the two elliptical axes are the same as 𝜎. For
most of the applications, a family of 𝑈×𝑉 Gabor
wavelets is usually required to perform the multiresolution and multi-orientation analysis, which
is defined as below [14]:
..(4)
Fig. 1 Gabor wavelets with 5 scales and 8 orientations
2.2 Wavelet Transform
Wavelets are functions that satisfy certain
mathematical requirements and are used in
presenting data or other functions, similar to
sines and cosines in the Fourier transform.
However, it represents data at different scales
or resolutions, which distinguishes it from the
Fourier transform.
Wavelet transform is an increasingly popular
tool in computer vision and image processing.
Many applications, such as compression,
detection, recognition, image retrieval have
been investigated. Wavelet transform has nice
features of space-frequency localization and
multi-resolutions. The wavelet transform of
a 1-D signal f(x) is defined as:
…(5)
The mother wavelet Ψ has to satisfy the
admissibility criterion to ensure that it is a
localized zero-mean function. Equation (5)
can be discretized by restraining α and b to a
discrete lattice. Typically, some more
constraints are imposed on Ψ to ensure that
the transform is non-redundant, complete and
constitutes a multi-resolution representation of
the original signal. 2-D DWT is generally carried
out using a separable approach, by first
calculating the 1-D DWT on the rows, and then
the
1-D
DWT
on
the
columns
:
DWTn[DWTm[x[m,n]].
Two-dimensional
Wavelet Transform decomposes an image into 4
“sub-bands” that are localized in frequency and
orientation, by LL, HL, LH, HH.
LL1
HL1
LH1
HH1
(a)
LL2
HL2
HL1
LH2
HH2
LH1
HH1
(b)
LL3
HL3
LH3
HH3
frequency component of the image. Second
level decomposition can then be conducted on
the LL sub band. Fig.2(b) shows a two-level
wavelet decomposition of different images of
size 112X92 pixels.
Earlier studies concluded that information in
low spatial frequency bands play a dominant
role in face recognition. Nastar et al. has
investigated
the
relationship
between
variations in facial appearance and their
deformation spectrums [15]. They found that
facial expressions and small occlusions affect
the intensity manifold locally. Under
frequency-based representation, only highfrequency spectrum is affected, called highfrequency phenomenon. Moreover, changes in
pose or scale of a face affect the intensity
manifold globally, in which only their lowfrequency spectrum is affected, called lowfrequency phenomenon.
Further decomposition to the LL sub-band
(two-level decomposition), leads to lower
dimensionalities and a multi resolution image.
We performed three level decomposition
which as shown in fig. 2(c). The number of
levels we choose depends on our work and
need.
HL2
HL1
LH2
LH1
HH2
HH1
(c)
Fig.2 Discrete Wavelet Transform : a) 1-D DWT b) 2 level 2-D
DWT c) 3 level 2-D DWT
Each of these sub bands can be thought of as a
smaller version of the image representing
different image properties. The band LL is a
closer approximation to the original image. The
bands LH and HL record the changes of the
image along horizontal and vertical directions,
respectively. The HH band shows the high
Fig.3 RMS error V.S. Threshold Pt. plot in MATLAB
The Root Mean Square (RMS) Error (also
called the root mean square deviation, RMSD)
is a frequently used measure of the difference
between values predicted by a model and the
values
actually
observed
from
the
environment that is being modeled. These
individual differences are also called
residuals, and the RMSE serves to aggregate
them into a single measure of predictive power.
The RMSE of a model prediction with respect to
the estimated variable Xmodel is defined as the
square root of the mean squared error:
RMSE 

n
i 1
( X obs,i  X mo del ,i ) 2
n
…(6)
Where, Xobs is observed values and Xmodel is
modeled values at time/place i.
3. ORL FACE DATABASE
(400 images of 10 different peoples)
AT&T Laboratories Cambridge was founded in
1986 as the Olivetti Research Laboratory, better
known as ORL. This directory contains a set of
faces taken between April 1992 and April 1994 at
the Olivetti Research Laboratory in Cambridge,
UK. There are 10 different images of 40 distinct
subjects. For some of the subjects, the images
were taken at different times, varying lighting
slightly, facial expressions (open/closed eyes,
smiling/non-smiling)
and
facial
details
(glasses/no-glasses). All the images are taken
against a dark homogeneous background and the
subjects are in up-right, frontal position (with
tolerance for some side movement).
The ORL Face Database was manually altering
the file format into Portable Grey Map (PGM)
using suitable image processing software. The
PGM format is chosen because it is a lowest
common denominator grayscale image file
format. The files are in PGM format and can be
converted into other image format like .png or
.tif. The size of each image is 92x112, 8-bit grey
levels. The images are organized in 40 directories
(one for each subject).
Fig. 4 Some face images from ORL database
4. EXPERIMENTAL RESULTS
We used MATLAB 7.6.0(R2008a) to
implement all the experiments of Gabor
wavelets and Wavelet decompositions on
different face images of ORL database. The
original images and its features of some face
images as shown below.
Fig.5 Original image from ORL database
(a)
(b)
(c)
(d)
Fig.8 Two level 2-D Wavelet Decomposition
(e)
(g)
(f)
(h)
Fig.6 Gabor Wavelets (a),(c),(e),(g) and its filters(b),(d),(f),(h)
on different values of theta
(𝜃=0, pi/4, pi/2, 3pi/4) from fig.5
We define the five parameters to extract the
features using Gabor Wavelet (filter) in Eq. 1.
The parameter values are theta=0; lambda=3.5;
gamma=0.3; sigma=2.8; psi=0. By changing the
values of 𝜃 from 𝜃 =0, pi/4, pi/2 and 3pi/4, the
extracted features of Gabor wavelets and its
filters are shown in figure 6. To find this result,
we used ORL face database.
Fig.7 1-D Wavelet Decomposition
Fig.9 Three level 2-D Wavelet Decomposition
Again ORL face database is used to find the
Wavelet decomposition results. Figure 7
shows the result of 1-D Wavelet
decomposition. One, Two and Three level 2Dimensional Wavelet Transform decomposes
an image into 4 “sub-bands” that are localized
in frequency and orientation, by LL, HL, LH,
HH.
The band LL is a very closer approximation to
the original image. The bands LH and HL
record the changes of the image along
horizontal
and
vertical
directions,
respectively. The HH band shows the high
frequency component of the image. Figure 8
shows two-level 2-D Wavelet decomposition
and Figure 9 shows a three-level 2-D Wavelet
decomposition of two different images of size
112X92 pixels.
Figure 10 shows the 3D plotted graph of
Multi-resolution Discrete Wavelet analysis in
MATLAB.
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Fig.10 Multi-resolution Discrete Wavelet 3D Plot in MATLAB
5. CONCLUSION AND FUTURE WORK
In this research paper we studied the Gabor,
Wavelet (filter) and Wavelet Transform with
respect to filtering the face feature and
decomposing
the
original
face
image
respectively. The 2-D Wavelet Transform is to
capture the variations in faces. Experimental
results on an extensive set of ORL face database
demonstrate the MATLAB implementation for
identification. Experiments conducted on various
face conditions, including different angles,
expressions etc.
Future work includes more improvement in
identification of number of face images. Further
implementing the new method with more multiresolution transforms and finding the transform
which will give maximum recognition rate. The
further research-orientation in the future is to
design the algorithm which gives more accurate
face recognition.
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