Iaetsd multi-view and multi band face recognition

Multi-View and Multi Band Face Recognition
Survey
Ms.L.MadhuMitha Ms. A.BhagyaLakshmi
PG Student, CSE Dept Asst.Prof, CSE Dept
Velammal Engineering College Velammal Engineering College
lmadhuviet@gmail.com kirubhagya@yahoo.com
Abstract - Face recognition is a challenging problem for security surveillance and become an active research
area during few decades. Due to the different levels of illumination conditions, variations due to lighting,
expression and aging, the recognition of such algorithms rate is considerably limited. To solve this
problem,multi-band face recognition algorithm is introduced in this paper. The multi-view and multi band face
recognition used in this paper is suitable for estimation the pose of the face from a video source. Unlike previous
eigenface or PCA approach, a small number (40 or lower) of eigenfaces are derived from a set of training face
images by using the Karhunen-Loeve transform or PCA. Instead, the similarity between feature sets from
different videos using Wavelet Transform, Entropy imaging is measured in this work. The experimental results
show that the wavelet transform takes less response time which is more suitable for feature extraction and face
matching with high accuracy, performance and accuracy in CBIR system.
Keywords: Image Processing, Face Recognition, Multi-View Videos, Wavelet Transform.
I. Introduction
A biometric system[4] provides automatic
recognition of an individual based on some sort of
unique feature or characteristic possessed by the
individual. Behavioral biometrics includes
signatures, voice recognition, gait measurement,
and keystroke recognition. Physiological
biometrics includes facial recognition,
fingerprinting, hand profiling, iris recognition,
retinal scanning, and DNA testing. Behavioral
methods tend to be less reliable than physiological
methods because they are easier to duplicate than
physical characteristics (Jain et al., 1999).
Physiological attributes are more trusted method in
biometrics among which iris recognition is gaining
much attention in accuracy and reliability. First
automatic face recognition[2][3][5] system was
Developed by Kanade 1973.
A face recognition system is expected to identify
faces present in images and videos automatically. It
can operate in either or both of two modes: Face
verification (or authentication): involves a one-to-
one match that compares a query face image
against a template face image whose Identity is
being claimed. Face identification (or
recognition)[8][9]: involves One-to-many matches
that compare a Query face image against all the
template images in the database to determine the
identity of the query face. During face recognition
major challenges is Inter-class similarity and Intra-
class similarity. Inter-class similarity means people
having identified similar faces which make their
distinction difficult. And Intra-class variations
Causes some changes in head pose, illumination
conditions, expressions, facial accessories,
expressions, aging effects. Lighting conditions
change the face appearances so approaches based
on intensity images are not sufficient for
overcoming this problem.
II. Background concepts
A. Feature Recognition: Biometric facial
recognition systems[1][7] compare images of
individuals from incoming video against specific
databases and send alerts when a positive match
occurs. The key steps in facial recognition are: face
detection, recording detected faces, Match recorded
faces with those stored in a database automatic
process to find the closest match. Applications
include: 1. VIP lists –make staff aware of important
individuals (VIP) and respond in an appropriate
manner, 2. Black lists – identify known offenders
or to register suspects to aid public safety, 3.
Banking transactions - verification of the persons
attempting a financial transaction and so on.
Image Acquisition:
The image acquisition engine enables you to
acquire frames as fast as your camera and PC can
support for high speed imaging. Image is captured
using digital camera in RGB format. The first
function performed by the imaging system is to
collect the incoming energy and focus it onto an
image plane. Digital and analog circuitry sweeps
Proceedings of International Conference on Advancements in Engineering and Technology
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47

Fig.1 Multi-Band Face Recognition Processing
these outputs and Convert them to an analog signal,
which is then digitized by another section of the
imaging system. The output is a digital image is
formed finally.
Pre-Processing:
Image captured not used for feature Extraction and
classification, because captured face Images are
affected by various factors such as noise, lighting
variance, climatic conditions, poor resolutions of an
image, wanted background etc.
RGB Image to GRAY Scale Image:
RGB images converts to gray scale by eliminating
the hue and saturation information while retaining
the luminance. Then, add together 30% of the red
value, 59% of the green value, and 11% of the blue
value. To convert a gray intensity value to RGB,
simply set all the three primary color components
red, green and blue to the gray value, correcting to
a different gamma if necessary.
Filtering Techniques: Filtering refers to accepting
or rejecting certain frequency components. A filter
that passes low frequencies is called a lowpass
filter. The Net image produced by lowpass is to
blur (smooth) an image. Two Dimensional lowpass






0
0
),(0
),(1
),(
DvuDif
DvuDif
vuH
where D0 is specified nonnegative quantity.
A filter that passes high frequencies but reduce
amplitude Signal with frequency lower than the
sscutoff frequencies.






0
0
),(0
),(1
),(
DvuDif
DvuDif
vuH
where Do is the cutoff distance measured from the
origin Of the frequency plane.
Wavelets: Wavelets can be used to extract
information from many different kinds of data,
including – but certainly not limited to – audio
signals and images. Sets of wavelets are generally
needed to analyze data fully. A set of
"complementary" wavelets will decompose data
Camera
R-image
(600nm-700nm)
G-image
(500nm-600nm) Wavelet
Transform
Feature
ExtractionR-image
(400nm-500nm)
IR-image
(1000nm)
Database of Image Feature
Matching Face
ID
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without gaps or overlap so that the decomposition
process is mathematically reversible.
Wavelet transforms[10] are classified into discrete
wavelet transforms (DWTs) and continuous
wavelet transforms (CWTs). Both DWT and CWT
are continuous-time (analog) transforms. They can
be used to represent continuous-time (analog)
signals. CWTs operate over every possible scale
and translation where as DWTs use a specific
subset of scale and translation values or
representation grid.
A continuous wavelet transform (CWT) is used to
divide a continuous-time function into wavelets.
Unlike Fourier transform, the continuous wavelet
transform possesses the ability to construct a time-
frequency representation of a signal that offers very
good time and frequency localization.
A discrete wavelet transform (DWT) is any wavelet
transform for which the wavelets are discretely
sampled. As with other wavelet transforms, a key
advantage it has over Fourier transforms is
temporal resolution: it captures both
frequency and location information (location in
time).
Haar Wavelets
The first DWT was invented by the Hungarian
mathematician Alfréd Haar. For an input
represented by a list of n
2 numbers, the Haar
wavelet transform [10] may be considered to
simply pair up input values, storing the difference
and passing the sum. This process is repeated
recursively, pairing up the sums to provide the next
scale: finally resulting in 12 n
differences and one
final sum.
B. Feature Extraction: When the input data is too
large to be processed then the input data will be
transformed into a reduced representation set of
features. Transforming the input data into the set of
features is called feature extraction. If the features
extracted are carefully chosen it is expected that the
features set will extract the relevant information
from the input data in order to perform the desired
task using this reduced representation instead of the
full size input.
Principal Component Analysis
After feature extraction is performed feature
vectors are need to minimize. Principal component
analysis (PCA)[8] is a statistical procedure that
uses an orthogonal transformation to convert a set
of observations of possibly correlated variables into
a set of values of linearly uncorrelated variables
called principal components. The number of
principal components is less than or equal to the
number of original variables.
The steps involved in PCA can be summarized as
obtain the input matrix; calculate and subtract the
mean; calculate the covariance matrix; the
Eigenvectors; Eigen values and then forming a new
feature vector; once the new feature vector is
formed; the new dataset with low dimensions is
derived. The new feature vectors are passed to
classifier.
Database Image
To use a standard test data set for researchers to be
able to directly compare the results. While there are
many databases in use currently, the choice of an
appropriate database to be used should be made
based on the task given (aging, expressions,
lighting etc).
Another way is to choose the data set specific to the
property to be tested (e.g. how algorithm behaves
when given images with lighting changes or
images[6] with different facial expressions). If, on
the other hand, an algorithm needs to be trained
with more images per class (like LDA), Yale face
database is probably more appropriate than
FERET. Some face data sets often used by
researchers:
1.The Color FERET Database, USA: The images
were collected in a semi-controlled environment.
To maintain a degree of consistency throughout the
database, the same physical s etup was used in each
photography session. Because the equipment had to
be reassembled for each session, there was some
minor variation in images collected on different
dates.
2. SCface - Surveillance Cameras Face Database:
SCface is a database of static images of human
faces. Images were taken in uncontrolled indoor
environment using five video surveillance cameras
of various qualities.
3. Natural Visible and Infrared facial Expression
database (USTC-NVIE): The database contains
both spontaneous and posed expressions of more
than 100 subjects, recorded simultaneously by a
visible and an infrared thermal camera, with
illumination provided from three different
directions. The posed database also includes
expression images with and without glasses.
C. Feature Matching: If the template image has
strong features, a feature-based approach may be
considered; the approach may prove further useful
if the match in the search image might
be transformed in some fashion. Since this
approach does not consider the entirety of the
template image, it can be more computationally
efficient when working with source images of
ISBN NO : 978 - 1502893314
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49

larger resolution, as the alternative approach,
template-based, may require searching potentially
large amounts of points in order to determine the
best matching location
III. Conclusion
Face recognition technology has come a long
way for recognising people. Normally the face
images are not accurate in single view videos as it
does not support pose variations, illumination
changes and so on. Hence in order to provide better
performance, this work presents the combination of
taking Multi -View videos, IR image and Wavelet
Transform[10]. Multi -View videos and IR image
provides the advantage of overcoming the
environmental constraints and providing more
accurate image in all conditions when compared
with RBG image which provides accurate image
only at normal lighting conditions. Wavelet
Transform removes redundancies and preserves the
originality of the image at multi scales and multiple
directions. Thus our approach helps in feature
extraction and face matching with high accuracy
and less response time.
IV. References
1. P. Viola and M. J. Jones, “Robust real-time face
detection,” Int. J. Comput.Vis., vol. 57, pp. 137–
154, May 2004.
2. A.C. Sankaranarayanan, A. Veeraraghavan, and
R. Chellappa, “Object detection, tracking and
recognition for multiple smart cameras,”
Proc.IEEE, vol. 96, no. 10, pp. 1606–1624, Oct.
2008.
3.A. Li, S. Shan, and W. Gao, “Coupled bias-
variance tradeoff for crosspose face recognition,”
IEEE Trans. Image Process., vol. 21, no. 1, pp.
305–315, Jan. 2012.
4. A.K. Jain, R. Bolle and S. Pankanti, “Biometrics:
Personal Identification in Network Society,”
Kluwer Academic Publishers, 1999.
5. V. Blanz and T. Vetter, “Face recognition based
on fitting a 3D morphable model,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 25,no. 9, pp. 1063–
1074, Sep. 2003.
6. P. Breuer, K.-I. Kim, W. Kienzle, B. Scholkopf,
and V. Blanz, “Automatic 3D face reconstruction
from single images or video,” in Proc. IEEE Int.
Conf. Autom. Face Gesture Recognit., Sep. 2008,
pp.
7. A. Pentland, B. Moghaddam, and T. Starner,
“View-based and modular eigenspaces for face
recognition,” in Proc. IEEE Conf. Comput. Vis.
Pattern Recognit., Jun. 1994, pp. 84–91.
8. V. Blanz and T. Vetter, “Face recognition based
on fitting a 3D morphable model,” IEEE Trans.
Pattern Anal. Mach. Intell., vol. 25,no. 9, pp. 1063–
1074, Sep. 2003.
9. P. Breuer, K.-I. Kim, W. Kienzle, B. Scholkopf,
and V. Blanz, “Automatic 3D face reconstruction
from single images or video,” in Proc. IEEE Int.
Conf. Autom. Face Gesture Recognit., Sep. 2008,
pp. 1–8.
10. A. Pentland, B. Moghaddam, and T. Starner,
“View-based and modular eigenspaces for face
recognition,” in Proc. IEEE Conf. Comput. Vis.
Pattern Recognit., Jun. 1994, pp. 84–91.
11.Z.Dezhong., C.Fayi, “Face Recognition based
on Wavelet Transform and Image Comparison,”
International Symposium on Computational
Intelligence and Design, 2008.
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Iaetsd multi-view and multi band face recognition

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