Comparative Analysis of Face Recognition using DCT

International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 3, Issue 5, August 2014
Comparative Analysis of Face Recognition using DCT, DWT and PCA for
Rotated faces
Debaraj Rana1, Sunita Dalai2, Bhawna3, Sujata Minz4, N. Prasanna5, Tapasri Tapasmita Sahu6
1,2
Department of ECE, Asst. Professor, Centurion University of Technology & Management, Odisha, INDIA
Department of ECE, B.Tech Students, Centurion University of Technology & Management, Odisha, INDIA
3, 4, 5, 6
ABSTRACT
Face recognition has been an active research area in
the pattern recognition and computer vision domains.
Human’s day to day actions are increasingly being
handled electronically, instead of face to face. Face is a
complex multidimensional structure and needs good
computing techniques for recognition. The main aim of
Face Recognition system is to retrieve face images
which are similar to a specific query face image in large
face Databases. The retrieved face images can be used
for many applications. In this paper we have done face
recognition using Discrete Cosine Transform (DCT) and
Discrete Wavelet Transform (DWT) and Principal
Component Analysis (PCA). to varieties of test images
which are rotated 15° towards right, 15° towards left,
30° towards right, 30° towards left, with low
illumination and different facial expressions. Then we
have developed a comparative analysis between all the
three techniques based on recognition rate.
Keywords - DCT, DWT, Face Recognition, PCA
I.
consuming and less efficient. It is better get unique and
important information and discards other useless
information in order to make system efficient. Face
recognition [6] systems can be widely used in areas
where more security is needed. Researchers have
developed varieties of new techniques to improve the
face recognition rate. Also different people gone for
different approaches like Geometric /Template Based
Approaches,
Piecemeal/
Holistic
Approaches,
Template/Statistical/ Neural Network Approaches [22].
In this paper we have done the face recognition using
statistical and frequency domain approach. We have
applied DCT, DWT and PCA for face recognition. Face
recognition techniques applied to FEI database from
which we have taken 180 test images and these test
images are of different varieties like frontal face with
different expression, low illumination also some are
rotated 15O, 30O towards right as well as left. After
applying all the three technique to the test images then
we have developed a comparative analysis among the
three techniques based on the recognition rate.
INTRODUCTION
II. DISCRETE COSINE TRANSFORM (DCT)
Face recognition (FR) has emerged as one of the most
extensively studied research topics that spans multiple
disciplines such as pattern recognition, signal processing
and computer vision [6]. This is due to its numerous
important applications in identity authentication, security
access control, intelligent human-computer interaction,
and automatic indexing of image and video databases.
Face recognition has repeatedly shown its importance
over the last ten years or so.
Not only is it a vividly researched area of image
analysis, pattern recognition and more precisely
biometrics but also it has become an important part of
our everyday lives since it was introduced as one of the
identification methods to be used in e-passports and in
many security purposes. The human face is full of
information but working with all the information is time
DCT has emerged as a popular transformation
technique widely used in signal and image processing.
This is due to its strong “energy compaction” property:
most of the signal information tends to be concentrated
in a few low-frequency components of the DCT [2, 16].
DCT transforms the input into a linear combination of
weighted basis functions. These basis functions are the
frequency components of the input data. DCT is similar
to the discrete Fourier transform (DFT) in the sense that
they transform a signal or image from the spatial domain
to the frequency domain, use sinusoidal base functions,
and exhibit good de-correlation and energy compaction
characteristics. The major difference is that the DCT
transform uses simple cosine-based basis functions
whereas the DFT is a complex transform and therefore
stipulates that both image magnitude and phase
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852
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 3, Issue 5, August 2014
information be encoded The general equation for the
DCT of an NxM image age f (x, y) is defined by the
following equation [2]:
vertical features of outline and nose are clearer than its
horizontal features, depicts face pose features. [18]
(1)
The size of input image is N x M and f(i, j) is the
intensity of pixel at x(i, j). F(u, v) is the DCT coefficient
for the pixel at x(i,j). The DCT of an image is shown
figure 1 below.
[Fig.2 Two Level Decomposition using down sampling]
[Fig.1 DCT of a face Image]
III.
DISCRETE WAVELET TRANSFORM
(DWT)
[Fig. 3 Three Level Wavelet Decomposition]
Wavelets have many advantages over other
mathematical transforms such as the DFT or DCT.
Functions with discontinuities and functions with sharp
spikes usually take substantially fewer wavelet basis
functions than sine-cosine functions to achieve a
comparable approximation. Wavelets ability to provide
spatial and frequency representations of the image
simultaneously motivates its use for feature extraction
The Haar wavelet transform is a widely used technique
that has an established name as a simple and powerful
technique for the multi-resolution decomposition of time
series.
An original image of size N x N is first of all pass
through a filter horizontally and vertically as shown in
figure 2. The low pass filtering in horizontal direction
and high pass filtering in vertical direction gives rise to
LH component, likewise filtering gives rise to four
components LL, LH, HL and HH during first level of
decomposition [3, 17].
The LL component which represents the approximate
coefficients of the decomposition is used to produce next
level of decomposition. The sub band HL represents
major facial expression features. The sub band LH, the
The sub band HH is the unstable band in all sub bands
because it is easily disturbed by noises, expressions and
poses. And the sub band LL will be the most stable sub
band. Here an image and its detail and approximate
coefficients are shown in figure 4.
(a)
(b)
[Fig. 4 (a) An Face image (b) One Level Decomposed DWT
output]
IV. PRINCIPAL COMPONENT ANALYSIS
(PCA)
The Principal Component Analysis (PCA) is one of
the most successful techniques that have been used in
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International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 3, Issue 5, August 2014
image recognition and compression. PCA is a statistical
method under the broad title of factor analysis [7]. The
purpose of PCA is to reduce the large dimensionality of
the data space (observed variables) to the smaller
intrinsic dimensionality of feature space (independent
variables), which are needed to describe the data
economically.
The main idea of using PCA [5] for face recognition
is to express the large 1-D vector of pixels constructed
from 2-D facial image into the compact principal
components of the feature space. This can be called
eigenspace projection. Eigenspace is calculated by
identifying the eigenvectors of the covariance matrix
derived from a set of facial images (vectors) [7]. After an
image has been projected in the eigenspace, a feature
vector containing the coefficients of the projection [20]
is used to represent the image.
The algorithm is as follows:
Each image I(x, y) will be represented as a NxM vector
Γi and then average face Ψ is computed as :
(2)
reconstructed from its projection coefficients and the
eigenvectors. The Eigen faces of a set of faces of the
database are shown in figure 5.
[Fig. 5 Eigen faces using PCA]
V.
PROPOSED METHOD
Database: We have considered FEI database [23]
and create the face database by manually cropping the
face images. The face database contains 50 face images
with dimension 144x104. Then the test images are
divided in to six category with each category contain 30
face image with dimension 144x104. The six varieties
database contain face images rotated 30o towards right as
well as towards left, 15o towards right as well as towards
left, also with low illumination and different facial
expression. We have applied each of the method
separately to the database. Accordingly with different
test images we found out the recognition result.
where R represents the number of faces in the raining
set. Then mean subtracted face image as Φi= Γi- Ψ.
After the covariance matrix is calculated as
(3)
Where A=[Φ1 Φ2 . …. ΦR]. The eigenspace can be
calculated by calculating eigenvectors Vi of C, but as
the size of C (N2xN2) is very large, so instead Vi we
have to calculate Ui which is eigen vector of ATA where
Vi and Ui related as Vi=AUi For Eigenspace we have to
consider K best eigenvector of Vi.[19, 22]
An unknown face can be recognize after normalize
Ф=Γ-Ψ and projecting on eigenspace as
[Fig.6 Test images from FEI Database]
(4)
Where wi is the coefficient of projection. The projection
coefficients allow us to represent images as linear
combinations of the eigenvectors. It is well known that
the projection coefficients define a compact image
representation and that a given image can be
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[Fig. 7 Sample of Face database]
854
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
DCT
Feature
Extraction
Feature
Selection
Test
Images
DCT
Feature
Extraction
Feature
Selection
Face Recognized
Training
Images
Distance
Measurement
The algorithm process for recognition using DCT is as
follows.
[Fig.8 Face Recognition algorithm using DCT]
Test
. Images
DWT
DWT
Feature
Extraction
Feature
Selection
Feature
Extraction
Feature
Selection
Face Recognized
Training
Images
Distance
Measurement
(b) Recognition using DWT
For recognition using DWT we have decomposed
the database images to 3 levels and extracted the
features, then the test images are gone for same process
and found out the features. The features then compared
with database features and recognition done. The
algorithm is as follows.
Training
Images
Projection on
Eigen Space
Coefficient of
Projection
Test
Images
Projection on
Eigen Space
Coefficient of
Projection
Face Recognized
(a) Recognition using DCT
We have first found out the DCT coefficient of all the
data base images then using the triangular selection
method [16] selected the best coefficients. Then same
process is applied to a test image and using Euclidean
distance we found out the recognition. The Euclidean
distance is given as
(5)
Distance
Measurement
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(b)
[Fig.10 (a) Procedure to create Eigen space, (b) Face
Recognition algorithm using PCA]
VI.
RESULT ANALYSIS
The experimental results have been developed by
taking all the test images which includes the frontal
faces as well as the rotated face images. All the test
images are gone through the three different techniques
PCA, DCT and DWT. We have found out the
recognition rate for each category of test images with
each technique. On the basis of the recognition rate we
have compared the three techniques.
In this experiment we tested the recognition rate for
all three techniques DCT, DWT and PCA. For every
single technique we calculated the recognition rate for
180 test images which are rotated 30 degree left as well
as towards right, 15 degree left as well as towards right,
faces with low illumination and faces with different
expressions. The recognition rate is calculated by the
formula:
Recognition Rate= (No. of image recognized correctly /
Total no. of test image) × 100
The recognition result as follows:
[Fig.9 Face Recognition algorithm using DWT]
(c) Recognition using PCA
The database image taken and from a large
dimension we have tuned to a low dimension using the
principal components. This low space is called
eigenspace. Then each database image is projected on
eigenspace and the coefficient of projection was found.
Likewise for a test image same coefficients of projection
calculate and face recognition done using Euclidean
distance. The algorithm as follows:
Training
Images
Mean
Image
Covariance
Matrix
Eigen
Vector
Eigen
Space
(a)
(b)
(c)
(d)
(e)
(f)
[Fig.11 Face Recognition result for Test Images (a) 15O
rotated to right (b) 15O rotated to left (c) 30O rotated to left (d)
(a)
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International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 3, Issue 5, August 2014
30O rotated to right (e) with less illumination of light (f) with
different facial expression]
develop same hybrid technique of all three to improve
recognition rate
REFERENCES
Journal Papers:
(a)
(b)
(c)
(d)
(e)
(f)
[Fig. 12 The comparative result for DCT, DWT and PCA with
test images (a) 15O rotated to right (b)15O rotated to left (c)
30O rotated to left (d) 30O rotated to right (e) with less
illumination of light (f) with different facial expression]
VII.
CONCLUSION
In this paper we have done the face recognition
using three methods DCT, DWT and PCA. For the
simulation we have taken FEI database. Here we have
considered a database of 30 frontal face images and 180
test images with six different categories like 30 degree to
left, 30 degree to right etc.
After recognition using all the three methods we
compared the recognition rate of each method with each
category and finally we have shown the comparative
result among all. In most cases DCT is giving good
results as compared to other techniques with the FEI
database.
In future we have planned to improve the techniques, so
that we can increase the recognition rate through the test
images which are rotated. Also we have planned to
[1].Anil kumar Katharotiya ,Swati Patel ,Mahesh
Goyani, “Comparative Analysis between DCT &
DWT Techniques of Image Compression”, Journal of
Information Engineering and Applications, Vol 1,
No.2, 2011.
[2].Ziad M. Hafed Martin D. Levine, “Face Recognition
Using the Discrete Cosine Transform” International
Journal of Computer Vision, 167–188, 2001.
[3].Jagadeesh H S Suresh Babu K, Raja K B, “DBC
based Face Recognition using DWT” Signal &
Image Processing : An International Journal (SIPIJ)
Vol.3, April 2012.
[4].M. Turk and A. Pentland, “Eigenfaces for Face
Detection/Recognition”, Journal of Cognitive
Neuroscience, vol. 3, 1991.
[5].Jon Shlens, “A tutorial on principal component
analysis Derivation”, Discussion and Singular Value
Decomposition, 25 March 2003.
[6].W. Zhao, R. Chellappa, P. J. Phillips, and A.
Rosenfeld, “Face Recognition: A Literature Survey,”
ACM Computing Surveys, vol. 35, no. 4, pp. 399458, 2003
[7]. Kyungnam Kim “Face Recognition using Principle
Component Analysis:” Department of Computer
Science University of Maryland, College Park MD
20742, USA 2003
[8].A. S. Tolba, A.H. El-Baz, and A.A. El-Harby “Face
Recognition: A Literature Review,” International
Journal of Signal Processing, vol. 2, no. 2, pp. 88103. 2006
[9].Saeed
Dabbaghchian,
Masoumeh
P.
Ghaemmaghami,
Ali
Aghagolzadeh"Feature
extraction using discrete cosine transform and
discrimination power analysis with a face
recognition technology" Original Research Article
Pattern Recognition, Volume 43, Issue 4, April
2010, Pages 1431-1440
[10].V. Vidya, Nazia Farheen, K. Manikantan, S.
Ramachandran,"Face Recognition using Threshold
Based DWT Feature Extraction and Selective
Illumination Enhancement Technique" Original
Research ArticleProcedia Technology, Volume 6,
2012, Pages 334-343
www.ijsret.org
856
International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 – 0882
Volume 3, Issue 5, August 2014
[11].G.M. Deepa, R. Keerthi, N. Meghana, K.
Manikantan, “Face recognition using spectrum
based feature extraction”, 8 October 2011
[12].S.T. Roohi, D.S. Sachin, K. Manikantan, S.
Ramachandran,"Feature
Accentuation
Using
Uniform Morphological Correction as Preprocessing Technique for DWT based Face
Recognition" Original Research ArticleProcedia
Technology, Volume 10, 2013, Pages 690-698
[13].Haifeng Hu, "Variable lighting face recognition
using discrete wavelet transform" Original Research
Article Pattern Recognition Letters, Volume 32,
Issue 13, 1 October 2011, Pages 1526-1534
[14].Vytautas Per libakas, "Distance measures for PCAbased face recognition" Pattern Recognition Letters,
Volume 25, Issue 6, 19 April 2004, Pages 711-724
[15].M.Turk,A.Pentland,”Eigenfacesfor
recognition”,
International Journalof Cognitive Neuroscience3
(1)(1991)71–86.
[16] K Manikantan et.al ,” Face Recognition using
Block-Based DCT Feature Extraction”, Journal of
Advanced Computer Science and Technology, 1 (4)
(2012) 266-283
[17] Rabab M. Ramadan and Rehab F. Abdel Kader,
“Face Recognition Using Particle Swarm
Optimization-BasedSelected Features”, International
Journal of Signal Processing, Image Processing and
Pattern Recognition Vol. 2, No. 2, June 2009, pp 5165
[18] Jian Huang Lai, Pong C. Yuen!, Guo Can Feng!,
“Face recognition using holistic Fourier invariant
feature”s, Pattern Recognition 34 (2001) 95}109
[19] D Rana, H K Nayak,"Eye Localization using PCA
Based Genetic Search", International Journal of
Inventive Engineering and Sciences (IJIES),
Volume-2, Issue-4, Page 28-32, March 2014
Proceedings Papers:
[20]Zehang Sun et.al,”Neural-network-based gender
classification using genetic search for eigen-feature
selection”, Proceedings of the 2002 International
Joint Conference on Neural Networks, 2002. IJCNN
'02. (Volume:3 ), 2433 - 2438
[21]A. S. Samra, S. E. Gad Allah, R. M. Ibrahim, “Face
Recognition Using Wavelet Transform, Fast Fourier
Transform and Discrete Cosine Transform,” Proc.
46th IEEE International Midwest Symp. Circuits and
Systems (MWSCAS'03), vol. 1, pp. 272- 275, 2003.
[22] D. Rana and Dr. N. P. Rath, “Face Identification
using Soft Computing Tool “, IEEE International
Conference,
Conference
on
Advanced
Communication
Control
and
Computing
Technologies
(ICACCCT-2012),
Tamilnadu,
INDIA, Aug 23-25. Conference Proceeding Page(s):
232-236, 2012
Web Link
[23].http://fei.edu.br/~cet/facedatabase.html
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