50220130402003

International Journal of Graphics and Multimedia OF GRAPHICS
(IJGM),
0976 – 6448(Print),
INTERNATIONAL JOURNALDecember ISSN © IAEME AND
ISSN 0976 – 6456(Online) Volume 4, Issue 2, May 2013,
MULTIMEDIA (IJGM)
ISSN 0976 - 6448 (Print)
ISSN 0976 -6456 (Online)
Volume 4, Issue 2, May - December 2013, pp. 20-30
© IAEME: www.iaeme.com/ijgm.asp
Journal Impact Factor (2013): 4.1089 (Calculated by GISI)
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IJGM
©IAEME

TEXTURE ANALYSIS FOR FACE RECOGNITION
J. V. Gorabal
Associate Professor, CSE SCEM, Mangalore
Manjaiah D. H.
Professor, Computer Science Department. Mangalore University, Mangalore

ABSTRACT
A new approach for face recognition using wavelet features is presented. Initially, the
given image is divided into 12 blocks, each of size 50*60 pixels. Then, discrete wavelet
transform is applied to each block and energy features (mean) of horizontal and vertical
coefficients are determined. The extracted features from training samples are used to train the
neural network. Further, the test face image is processed to obtain wavelet energy features
and recognized using neural network classifier.
Keywords: Wavelet energy features, Neural Network, Face Recognition
1.

INTRODUCTION

Pattern recognition is a day machine intelligence problem with numerous applications
in a wide field, including Face recognition, Character recognition, Speech recognition as well
as other types of object recognition. The field of pattern recognition is still very much in it is
infancy, although in recent years some of the barriers that hampered such automated pattern
systems have been lifted due to advance in computer hardware providing machines capable
of faster and more complex computation.
Humans do face recognition on regular basis naturally and so effortlessly that we
never think of what exactly we the looked at in the face. Face is a dimensional object that is
subjected to varying illumination, poses, expressions and so on its two dimensional image.
Hence, Face recognition is an intricate visual pattern problem which can be operated in these
modes.
20

International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 2, May - December 2013, © IAEME
•

•

•

Face Verification (or Authentication) :
That compares a query face image against a template face image whose identity is
being claimed (i.e; one to one).
Face Identification (or Recognition)
That compares a query face image against all the template images in the database
to identify the query face.
Watch list that compares a query face image only to a list of suspects.

Authentication plays a very critical role in security-related applications like Ecommerce. The previously used authentication techniques like security pass or password will
not provide absolute confidence as they could be stolen and passwords are sometimes
(unwisely) written down. To overcome this drawback biometric security systems are uses.
The primary benefit to using face recognition is that facial features are more distinct
from one person to another person and these features have scored highest compatibility in
Machine Readable Travel Documents (MRTD), hence we go for face recognition.
The applications of face recognition are,
• Identity verification for physical access control in buildings or security areas is one of
the most common face recognition applications.
• To allow secure transactions through the Internet, face verification may be used
instead of electronic means like password or PIN numbers, which can be easily stolen
or forgotten.
• Face identification has also been used in forensic applications for criminal
identification (mug-shot matching) and surveillance of public places to detect the
presence of criminals or terrorists (for example in airports or in border control).
• It is also used for government application like national ID, driver`s license, password
and border control, immigration, etc.
The rest of the paper is organized as follows; the detailed survey related to character
recognition of text in scene images is described in Section 2. The proposed method is
presented in Section 3. The experimental results and discussions are given in Section 4.
Section 5 concludes the work and lists future directions of the work.
2.

RELATED WORKS

The Face Recognition is of the most difficult task, we have many approaches
proposed for the feature extraction [1].This paper explores the use of morphological operators
for feature extraction in range images and curvature maps of connected part They describe
two general procedures. The first is the identification of connected part boundaries for convex
structures, which is used to extract the node outline and the eye socket outlines of the face.
The part boundaries are dined locally based on minima of minimum principal curvature on
the surface. The locus of these points suggests boundary lines which surround most convex
regions on the surface. However, most of these boundaries are not completely connected. To
remedy this problem, w developed a general two-step connection procedure: the partial
boundaries are first dilated in such a way that the gaps between them are led. Second, the
resulting dilated outlines are skeletonized with the constraint that the pixels belonging to the
original boundary parts cannot be removed.

21

Feature extraction based on descriptive statistics [2]. This paper proposes a new
method of feature extraction for face recognition based on descriptive statistics of a face
image. This method works by first converting the face image with all the corresponding face
components such as eyes, nose and mouth to grayscale images. The features are then
extracted from the grayscale image, based on descriptive statistics of the image and its
corresponding face components. The edges of a face image and its corresponding face
components are detected by using the canny algorithm. In the recognition step, different
classifiers such as Multi Layer Perception (MLP), Support Vector Machine (SVM), kNearest Neighbors (k-NN) and Pair Opposite Class-Nearest Neighbor (POC-NN) can be used
for face recognition. They evaluated this method with more conventional eigenface method
based upon the AT & T and Yale face databases. The evaluation clearly confirm that for both
databases our proposed method yields a higher recognition rate and requires led
computational time than the eigen face method.
A method to extract facial features using improved deformable templates is described
[3]. This method include two steps, first locating features using rectangle templates designed
by myself; them, extracting features using deformable templates. In the first step, they get
rectangle block including facial features from facial images, the rectangle block is our
template to locate features. In the second step extracting features, they describe the features
of interest by a parameterized template, they design energy function which links with edges,
weighted grads, weighted variance and etc, when the energy function gets its minimum, the
parameter values can be a good description for facial feature. The experiment results show
that this arithmetic can extract facial feature better and more quickly. A novel face
recognition method based on Gabor-wavelet and linear discriminate analysis (LDA) is
proposed in [4]. Given training face images, discriminant vectors are computed using LDA.
The function of the discriminant vectors is two-fold. First, discriminant vectors are used as a
transform matrix, and LDA features are extracted by projecting original intensity images on
to discriminant vectors. Second, discriminant vectors are used to select disrciminant pixels,
the number of which is much less than that of a whole image. Gabor features are extracted
only on these discriminant pixels. Then, applying LDA on the Gabor features, one can obtain
the Gabor-LDA features. Finally, a combined classifier is formed based on these two types of
LDA features.
Hidden Markov model (HMM) is a promising method [5] that works well for images
with variations in lighting, facial expression, and orientation. Face recognition draws
attention as a complex task due to noticeable changes produced on appearance by
illumination, facial expression, size, orientation and other external factors. To process images
using HMM, the temporal or space sequences ate to be considered. In simple term HMM can
be defined as set of finite states with associated probability distributions. Only the outcome is
visible to the external user not the and hence the name Hidden Markov Model. The work in
the method deals with various techniques and methodologies used for resolving the problem.
A face recognition system for personal identification and verification using Genetic
Algorithm and Back-propagation Neural Network is proposed [6]. The system consists of
three steps. At the very outset pre-processing are applied on the input image. Secondly face
feature are extracted, which will be taken as the input of the Back-propagation Neural
Network (BPN) and Genetic Algorithm (GA) in the third step and classification is carried out
by using BPN and GA. The proposed approaches are tasted on a number of face images.
Experimental results demonstrate the higher degree performance of this algorithm.

22

Biometric technology has been frequently utilized be researchers in identifying and
recognizing human features [7]. This technology identifies human`s unique and static body
parts, such fingerprints, eyes, and face. The identification and recognition of a human face
use face features` processing and analysis. This consists of determining face components`
region and their characteristics, which establishes the role of individual component in face
recognition. This research develops a system that separates face features into face
components, and extracts the eyes, nose, mouth and face boundary. This process conducted
on a frontal single still image. Distances between components are measure, and then
combined with other features to construct face semantic.
Distances between features are determined by going through the process of face
detection based on skin color, cropping to normalize face region, and extraction of eyes, nose
and mouth features. This research shows that the determination of face features and face
components` distances can be used to identity a face a subsystem of a face recognition
system.
In this face recognition research [8], the head is fixed when a photograph is taken.
The infrared diodes provide the only illumination. In front of the CCD camera, a light filter
lens is used to filter all other light. After the photograph is taken, the eyebrows, eyes, lips,
and contour are extracted separately. The shape, size, object-to-object distance, center and
orientation are found for each extracted object. The techniques to solve the object shifting
and rotating problem are investigated. Image subtraction is used to examine the geometric
difference of the two different faces. The obtained classifying data in this research can
accurately classify different people`s faces.
We propose a fast and improved facial feature extraction technique [9] for embedded
face-recognition applications. First, we introduce local texture attributer to a statistical face
model. A texture attribute characterizes the 2-D local feature structures and is used to guide
the model deformation. This provides more robustness and faster convergence than with
conventional ASM (Active Shape Model). Second, the local texture attributes are modeled by
Haar-wavelets, yielding faster processing and more robustness with respect to low-quality
images. Third, we use a gradient-based method for model initialization, which improves the
convergence. We have obtained good results dealing with test faces that are quite dissimilar
with the faces used for statistical training. The convergence area of our proposed method
almost quadruples compared to ASM. The Haar-wavelet transform successfully compensates
for additional cost of using 2-D texture features. The algorithm has also been tested in
practice with a webcam, giving (near) real-time performance and good extraction results.
The extraction of required features from the facial image is an important primitive
task for face recognition. The paper [10] evaluates different nonlinear feature extraction
approaches, namely wavelet transform, radon transform and cellular neural networks (CNN).
The scalability of the linear subspace techniques is limited as the computational load and
memory requirements increase dramatically with the large database. In this work, the
combination of radon and wavelet transform based approach is used to extract the multiresolution features, which are invariant to facial expression and illumination conditions. The
efficiency of the stated wavelet and radon based nonlinear approaches over the databases is
demonstrated with the simulation results performed over the FERET database. This paper
also presents the use of CNN in extracting the nonlinear facial features. The detailed
description of the proposed methodology is given in the next section.

23

3.

PROPOSED METHODOLOGY FOR FACE RECOGNITION

The proposed method uses feed-forward back propagation neural network based
classifier for classification. The methodology is shown in Fig 1. The method involves two
phase namely training phase and testing phase. The detailed description of each phase is
given in the following sub sections.
3.1 Training
This involves processing of images of different person with different expressions,
extracting their features and finally developing suitable neural network models which
recognize the different persons. The classification makes use of features extracted using
discrete wavelet transform approach form face image samples. The original images are
converted into gray scale images. Each image is divided into 12 blocks of size 50*60.For
each block Discrete Wavelet Transform is applied. 24 features, 2 from each block are
extracted.
The neural network architecture that is most commonly used with the back
propagation algorithm is the multilayer feed-forward network. In training phase the artificial
neural network is trained using Back Propagation feed forward neural model. Two pair of
files, “input” and “output” are generated. These two pair of files is then given to the neural
network which then trains itself accordingly. The training takes place such that the neural
network learns that the neural network learns that each entry in the input file has a
corresponding entry in the output file.

Fig .1. Proposed Block Diagram for Recognition of face
3.2

Testing
In testing input image from testing set is selected and its features are extracted and
given them to the trained model, the trained ANN model classifies given sample as
corresponding person.
3.3

Database
Face images of 20 different person with 20 different expressions are collected. Each
image is of size 200*180 in .jpg format. Database consists of frontal face images and with
same background. The sample images are shown in Fig 2.
24


Fig .2. Sample Images
3.4

Feature Extraction
A pattern is a quantitative or structural description of an object or an entity of interest
in an image. One or more descriptors of an object or an entity an image from the pattern. In
other words, a pattern is an arrangement of descriptors. The descriptors also called features in
pattern recognition literature. The features are necessary for differentiating one class of
objects from another. A method must be used for describing the objects so that features of
interest are highlighted. The description is concerned with extracting of features from the
object/entity of an image.
Algorithm for feature extraction
Input : sample image
Output : Array containing features
Step 1 : Convert the RGB in to gray-scale image
Step 2 : Divide the image into 12 blocks of size 50*60
Step 3 : for i=1 to 12
Apply dwt2 for block
Calculate the energy function of horizontal and vertical co-efficient End.
Step 4 : These co-efficient are stored in an array.
Each image is of size 200*180. The original image is converted into gray scale image. Each
image is divided into 12 blocks each of size 50*60. For each block, Discrete Wavelet
Transform is applied. It computes approximation coefficients matrix and details coefficients
matrices (horizontal, vertical, and diagonal, respectively), of each block of the image. The
next page shows the 12 blocks with first level decomposition.
The discrete wavelet transformation is applied using the function dwt2
[ a h v d ] = dwt2 (m, `haar`);
Where m = block image
a = approximation co-efficient,h= horizontal co-efficient
v= vertical co-efficient, d= diagonal co-efficient
Energy functions (mean) are calculated using equations (1) to (2):
(1)

25

(2)
3.5. Classification Model
The features are stored for each 15 image with different expressions of 20 different
persons. The classification is carried out using only one type of feature set that consists of all
24 features ie 2 features from each 12 blocks of the image. The output layer consists of 20
nodes represented in binary digits. The output is given in Table 1 for recognizing face.
Table 1. Output Pattern for Recognition
Person

Person

Output Pattern

Person 1

10000000000000000000

Person 11

00000000001000000000

Person 2

01000000000000000000

Person 12

00000000000100000000

Person 3

00100000000000000000

Person 13

00000000000010000000

Person 4

00010000000000000000

Person 14

00000000000001000000

Person 5

00001000000000000000

Person 15

00000000000000100000

Person 6

00000100000000000000

Person 16

00000000000000010000

Person 7

00000010000000000000

Person 17

00000000000000001000

Person 8

00000001000000000000

Person 18

00000000000000000100

Person 9

00000000100000000000

Person 19

00000000000000000010

Person 10

4.

Output Pattern

00000000010000000000

Person 20

00000000000000000001

EXPERIMENTAL RESULTS AND ANALYSIS

Face images of 20 different people with 20 different expressions are collected.
Database consists of frontal face images and with same background.
4.1. An Experimental Analysis for a Sample Face Image
Each image is of size 200*180. The original image is converted into gray scale image
as shown in Fig 3.

Fig. 3. a) A Sample Face Test Image

b) Gray Image

Each image is divided into 12 blocks each of size 50*60. For each block, Discrete
Wavelet Transform is applied. It computes approximation coefficients matrix and details
coefficients matrices (horizontal, vertical, and diagonal, respectively), of each block of the
image. The Fig 4 shows the 12 blocks with first level decomposition.
26

Block

First Level Decomposition of dwt2

Fig 4. The 12 blocks with first level decomposition

27

The Table 2 shows the recorded results of 5 persons with 5 different expressions.
TABLE 2. The recorded results of 5 persons with 5 different expressions

28

4.2. An Experimental Analysis dealing with various issues
Our database consists of total 400 images out of those 300 images have been used to
train the neural network and 100 images have been used for testing against the trained
images, and the following analysis have been obtained. From 300 trained images 297 images
are correctly matched only 3 images match is not found.
From 100 testing images 90 images have been perfectly matched. i.e; 10 images
matches are not found. Out for 400 total of 13 where mismatched, we have obtained an
accuracy of 96.75%. The overall performance of the system after conducting the
experimentation on the dataset is reported in Table 3.

Classifier
Neural Network
5.

TABLE 3. Overall System Performance
Total images
Misclassification
Accuracy (in %)
400
13
96.75

CONCLUSION

Finally we reach with conclusion. In this project we have designed one of the best
approaches to recognize the faces. This method uses wavelet transform for extracting feature
vectors. From the experimental results, it is seen that this method gets the best results
compared to the other face recognition methods, which are supposed to be the most
successive ones. This technique is not only computationally less extensive as compared with
other recognition techniques but also provides best recognition result of 96.75% on images
various constraints like sad, happy, sleepy, surprise, open/closed eyes, smiling and non
smiling face. Several open questions still remain in our face recognition system. The
robustness for image variation in rotations, illumination, etc. must be improved. Here, we
evaluated the recognition performance only for small database from the aspect of security
systems, such simple evaluations are less useful. Hence, the evaluation on the robustness for
the largest data sets is necessary in practical use.
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