To Study The Face Recognition using D-LBP
Saurabh Asija1, Asst. Prof. Rakesh Singh2
1Research Scholar (Computer Engineering Department), UCoE, Punjabi University
2Asst. Prof. (Computer Engineering Department), UCoE, Punjabi University
ABSTRACT
Facial recognition is a biometric which uses
computer software to determine the identity
of the individual. Face recognition falls into
the category of biometrics which is the
automatic recognition of a person using
distinguishing traits. In social intercourse
our main focus is on face, which plays a
major role in conveying identity and
emotions. Face recognition plays an
important role in many applications such as
security systems, credit card verification and
criminal identification. The process of
recognition mainly contains the image
capture, the face positioning, the image
preprocessing, and the face recognition and
the face recognition is the most important
stage in the process of recognition. Among
various kinds of face recognition algorithm,
Local binary pattern (LBP) face recognition
algorithm and its invariants have been
widely concerned [3].
Key words: face recognition, LBP, d-LBP,
recognition rate, histogram
I. INTRODUCTION
Image processing can be defined as any
form of signal processing which takes an
image as a input, which can be a video
frame or a photograph for which the output
can be an image itself or a set of its
characteristics or parameters closely related
to the image. Face recognition is a computer
technology for identity authentication by
comparing the information of human visual
features. Currently it is a hot topic in pattern
recognition and artificial intelligence, and
widely used in the identification, video
surveillance and other aspects.
Local binary pattern (LBP) is used
in computer vision for the purpose
classification. In Texture Spectrum model
LBP was proposed in 1990. LBP was first
described in 1994. (LBP) is a texture
operator simple yet efficient in use. In it
pixels of an image are labeled by calculating
the threshold of the neighbors of each pixel
and then consider the result in binary form.
Due to its computational simplicity and
discriminative power, it proves to be a better
approach in various applications.
Double coding Local binary patterns is an
invariant of LBP. In LBP we compute only
amplitude threshold θ while in d-LBP we
compute distance threshold too ƹ.
θ= 1/𝑝 ∑𝑝−1
𝑘=0 |ik − ic | ,p=4
ƹ= 1/𝑝 ∑𝑝−1
𝑘=0 ik − ic
II.
,p=4
FACE RECOGNITION SYSTEM
Generalized face recognition system have
different modules; Sensor, pre-processing,
feature extraction, template generator, postprocessing etc. A biometric system is
essentially a pattern recognition system that
acquires raw biometric data from a person
using single or multi sensor, and do some
pre-processing on that data, and extracts
features using feature extraction then
generate the template, and match the output
image (which is got after post-processing) to
store template in the database, and executes
an action based on the result of a
comparison.
The pre-processing process may involve
number of steps is given below.
i.
ii.
iii.
iv.
v.
vi.
vii.
viii.
Image size Normalization
Histogram Equalization
Enhancement
Median Filtering
High pass filtering
Background removal
Translational and rotational
normalization
Illumination normalization
C. Feature Extraction Module
After pre-processing, we get enhanced face
image, which is presented to extract the
important features in order to find the key
features that are going to be used for
classification. In other words, vector feature
or key feature which is sufficient for
representing a face image is extracted .
Face Recognition Process
A. Sensor
In Face recognition system a single or multi
sensor system is used to capture an image or
acquire all necessary data, like facial
features, expressions etc. It is the module
where the face image under consideration is
presented to the system. An acquisition
module can take an image in several
different environments.
B. Pre-processing
In pre-processing module, following steps
are carried out to pre-process the captured
image. It gets the samples ready for the
forthcoming blocks, reducing the noise and
even transforming the original signal to a
more readable one. An important property of
this block is that it tries to reduce lightning
variations among pictures. In this case,
samples are first resized to standard
dimensions according to a requirement of
N*N pixels.
D. Template Matching
Various methods are employed to match
templates against enrolment templates
assigning confidence levels to the strength
of each match attempt. If the score surpasses
a predefined level, the comparison is
deemed a match. In many cases, a series of
images is acquired and scored against the
enrolment, so that a user attempting 1:1
verification within a facial scan system may
have 10 to 20 match attempts taking place
within 1 to 2 seconds. This sets facial – scan
apart from most other biometrics.
E. Post-processing
After completing the above steps, postprocessing of the image is carried out. In
postprocessing, elements that are not used in
comparison algorithms are discarded in
template to reduce the file size
III.
APPLICATIONS
Areas
Information Security
Access Security
Biometrics
Law Enforcement
Personal Security
Commercial
Government
Forensic
Applications
Access Security (OS,
Database), Data
Privacy (e.g. Medical
records), User
authentication (Trading,
Online, Banking)
Secure Access
Authentication
(Restricted
Facilities) Permission
based system accesslog
or audit trails.
Personal Identification
(National IDs,
15 Passport, Driving
Licenses, Voter
Registration).
Video Surveillance
Suspect Identification
Suspect tracking
(Investigation) Simulated
aging Forensic
reconstruction of face
from remaining
Home video surveillance
systems,
Expression interpretation
(Driver
Monitoring System).
Computer Network
Login, Electronic Data
Security, e-commerce,
internet access, ATM,
credit card, Physical
access control, mobile
phone, medical records
management and
distance learning etc.
National ID card,
managing inmates in a
correctional facility,
drivers license, social
security, border control,
passport control etc.
Corpse Identification,
criminal
investigation, parenthood
IV. DOUBLE CODING LBP
Basic LBP operator only considers the
differences of gray values between center
pixel and neighborhood pixels, but the
amplitude relationship between center pixel
gray value and the neighborhood pixel gray
values are ignored. Because every pixel gray
value cannot be made full use of, it could
lead to a drop in the final recognition rate
when the face texture features are extracted.
Moreover, too much sampling points will
make the algorithm mo re comp licated,
which may result in the decrease of the rate
of recognition. Due to the above problems,
this paper presents a double coding local
binary pattern (d-LBP).Firstly, reducing the
complexity of the algorithm, the sampling
points of d-LBP operator is reduced to 4
from 8 of the basic LBP operator
30 8
50
x
8
x
20 38 75
20 38 75
95 40 10
x
40 x
Reducing Sample Size
Then, making full use of the relationship
among the gray values of each pixel within a
certain local area. So θ is defined as the
amplitude threshold and ƹ is defined as the
difference threshold [7].
θ= 1/𝑝 ∑𝑝−1
𝑘=0 |ik − ic | ,p=4
ƹ= 1/𝑝 ∑𝑝−1
𝑘=0 ik − ic
,p=4
Where : 𝑖𝑐 is the gray value of the center
pixel in the local area; 𝑖𝑘 shows the k-th
grey value of sampling point in the center
pixel neighborhood area; p is the number of
sampling points. Finally, in order to describe
the facial texture information in detail, the
basic LBP operator with one binary coding
is replaced by the improved LBP operator
with two binary encoding. The first binary
code is related with the difference between
the neighborhood pixels gray values and the
center pixel gray value. Compared with
difference threshold ( ƹ), if it is larger, the
binary code is 1, on the other hand, is
marked as 0. The second binary code is
related
with
amplitude
between
neighborhood pixels gray values and the
center pixel gray value. Compared with
amplitude threshold (θ), if it is larger, the
binary code is marked as 1, otherwise 0. As
shown in formula [7].
Nonparametric statistical method is used to
determine the histogram similarity between
samples after getting a d-LBP histogram.
(H1 (i) − H2 (i))2
φ (𝐻1 , 𝐻2 ) = ∑
H1 (i) + H2 (i)
2
𝑖
x
8
x
x
01
x
20 38 75
00
x
11
x
x
10
x
40
x
θ=1/4(0+37+2+18) = 22;
ƹ=1/4(8+75+40+20) -38 = -2;
d-LBP = 01111000 = 120
In order to fully improve the effectiveness of
the d-LBP operator and make better use of
d-LBP to describe face image.First of all,
the d-LBP map of face image should be
blocked; then, the images of each block are
calculated to get the histogram of d-LBP
respectively;finally,
each
partitioned
histograms is connected according to a
certain order to get a composite feature
vector, that is,the d-LBP histogram of
overall face image.
Where: is the training sample;𝐻1 (i)is the
sample to be classified; Similarity 𝐻2 (i) is
measured by the distance of 𝜑 2 , the smaller
the d istance, the more similar the two faces.
V.
CONCLUSION
Double coding local binary pattern (d-LBP)
algorithm of face recognition is developed
based on the basic LBP algorithm. Because
of the shortcomings of the basic LBP
algorithm, the d-LBP algorithm fully
considers the relationship between the center
pixel gray value and neighborhood pixels
grey values, and reduces the number of
sampling points. Experimental data shows
that the d-LBP algorithm can be more
comprehensive to extract the partial features
of the face image, and more efficient,
accurate and quick to get the local texture
feature information. In addition, higher
training speed and recognition rate can be
achieved.
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