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ISSN (ONLINE) : 2045 -8711ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING

NOVEMBER 2014VOL- 4 NO - 11

@IJITCE Publication

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.4 NO.11 NOVEMBER 2014

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UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: [email protected] Phone: +44-773-043-0249

USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626

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IJITCE PUBLICATION

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Vol.4 No.11

November 2014

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From Editor's Desk

Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. Everyday life throws at us an endless number of pattern recognition problems smells, images, voices, faces, situations and so on. Most of these problems we solve at a sensory level or intuitively, without an explicit method or algorithm. he techniques we considered the manipulation of the dynamic range of a given digital image to improve visualization of its contents. In this consider more general image enhancement. We introduce the concept of image filtering based on localized image sub regions (pixel neighbourhoods), outline a range of noise removal filters and explain how filtering can achieve edge detection and edge sharpening effects for image enhancement. The basic goal of image enhancement is to process the image so that we can view and assess the visual information it contains with greater clarity. Image enhancement, therefore, is rather subjective because it depends strongly on the specific information the user is hoping to extract from the image. The primary condition for image enhancement is that the information that you want to extract, emphasize or restore must exist in the image. Fundamentally, ‘you cannot make something out of nothing’ and the desired information must not be totally swamped by noise within the image. Perhaps the most accurate and general statement we can make about the goal of image enhancement is simply that the processed image should be more suitable than the original one for the required task or purpose. This makes the evaluation of image enhancement, by its nature, rather subjective and, hence, it is difficult to quantify its performance apart from its specific domain of application. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE


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Dr. Chee Kyun Ng Ph.D

Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D,

Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D

Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D

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Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. S.Pannirselvam M.Sc., M.Phil., Ph.D Associate Professor & Head, Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D.

Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. S.Prasath M.Sc., M.Phil., Ph.D

Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India. Dr. Bulent AcmaPh.D

Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.


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Contents

Face Image Analysis under Various Noisy Backgrounds Using Gaussian Median Filtering FFFFFFFF S.Prasath & Dr.S.PannirselvamFFFFFFFFFFFFFFFFFFFFFFFFF.FFFFFFFFF.[243]


243

Face Image Analysis under Various Noisy Backgrounds Using Gaussian Median

Filtering [GMF] S.Prasath

Ph.D (Research Scholar),Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India.

Email: [email protected] Dr.S.Pannirselvam

Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India.

Email: [email protected]

Abstract--- Today, image processing penetrates into

various fields, but till it is struggling in quality issues.

Hence, image enhancement came into existence as an

essential task for all kinds of image processings. Various

methods are been presented for gray scale image

enhancement, especially for face image. In this paper

various filters are used for face image enhancement.

However, the authenticity of noises that might insert into

an image document will affect the performance of face

recognition algorithms. Hence, different filtering

algorithms are presented for noise elimination using

various filtering algorithm. In order to improve of the

image quality Gaussian Median filtering has been applied.

The experimental result shows that this method provides

better enhancement in term of quality when compared

with the existing methods such as Mean filter, Wiener

Filter and laplacian filter. The peak Signal Noise Ratio

(PSNR) and Mean Square Error (MSE) are been used for

similarity measures.

Keywords— Mean Filter (MF), Median Filter, Wiener Filter, Gaussian Median Filter (GMF),PSNR, MSE.

I . INTRODUCTION

In the computer era there is a rapid growth in the field of

information technology and the security system was suffering

from various issues. Today, criminals have been entered into

the field of information technology called cyber crime. Lot of

security systems has emerged to solve the various security

issues such as password, username, secret codes, but failed

due to cyber attacks. In order to overcome such security issues

the biometric system has been emerged with various features

such as face recognition, fingerprints recognition, gait, palm

print, voice, signatures etc.

Every human being can identify a faces in a scene with no

effort, with an automated system such objectives are the very

challenging one due to various factors which affects the

quality of the image. Hence, face recognition system has been

used to verify the identity of an individual. It can be

accomplished by matching process using various methods and

features such as geometric, statistical, low-level features

which are derived from face images.

Since last decades, researchers are involved on face

recognition in image processing and they achieved so many

mile stone for this. Because face recognition is the critical

stage to identify the face in images due to pose, presence or

absence of structural components, facial expression,

occlusion, image orientation. Above all, Noise is prime factor

of reducing face recognition rate. Several methods have been

evolved to increase recognition rate.

Filters are widely accepted to remove impulsive and high

frequency noise for signal and image processing. The concept

of filtering has its roots in the use of the Fourier transform for

signal processing in the so-called frequency domain. Spatial

filtering term is the filtering operations that are performed

directly on the pixels of an image. The process consists simply

of moving the filter mask from point to point in an image at

each point (x, y) and the response of the filter at that point is

calculated using a predefined relationship.

II. RELATED WORK In recent years, considerable progress has been made in

the area of face recognition with the development of many

techniques. Even these techniques perform extremely well

under various constrain, the problem of face recognition in

uncontrolled by noisy environment remains unsolved. Image

noise can originate in film grain or in electronic noise in the

input device such as scanner digital camera, sensor and

circuitry or in the unavoidable shot noise of an ideal photon

detector. Noise affects the identification of images in

authentication and also in pattern recognition process. The

identification of the nature of the noise [1] is an important part

in determining the type of filtering that is needed for

rectifying the noisy image. Noise in imaging systems is

usually either additive or multiplicative [2]. The basic types of

noise can be further classified into various forms [3] such as

amplifier noise or Gaussian noise, Impulsive noise or salt and

pepper noise, quantization noise, shot noise, film grain noise

and nonisotropic noise.

A model [4] proposed with noise removal filtering

algorithms. Most of them follows certain statistical parameters

and know the noise types. Applying various a filtering

algorithms that is sensitive to additive noise to an image that

has been degraded by a multiplicative noise which does not

provide best results. Many algorithms have been developed to

remove salt & pepper noise in document images with different


244

performance in removing noise and retaining fine details of

the image.

Various filtering techniques exist to perform the inverse of

the imperfections in the degraded image [5], [6]. These

filtering techniques are application oriented. Some filtering

techniques perform better than the others techniques based on

the noise category. These filters are used in a variety of

applications [7] efficiently in preprocessing module.

III. NOISE TYPES The noise is characterized by its pattern and its

probabilistic characteristics. There is a wide variety of noise

types while this paper focus on the most important types they

are Gaussian noise, salt and pepper noise, poison noise,

impulse noise, speckle noise.

A) Gaussian Noise Gaussian noise is statistical noise that has its probability

density function equal to that of the normal distribution, which

is also known as the Gaussian distribution. In applications,

Gaussian noise is most commonly used as additive white

noise to yield additive white Gaussian noise.

B) Salt and Pepper Noise Salt and pepper noise is a form of noise typically seen on

images. It represents itself as randomly occurring white and

black pixels. Salt and pepper noise creep into images in

situations where quick transients, such as faulty switching,

take place.

C) Speckle Noise Speckle is a complex phenomenon, which degrades an

image quality. Speckle noise is a multiplicative noise. The

speckle noise follows a gamma distribution [8]. Thus,

denoising or reducing the noise from a noisy image has

become the predominant step in image processing. For the

quality and edge preservation of images we have taken

different denoising techniques into consideration.

IV. EXISTING METHODOLOGY 4.1 Filters

Generally filters are used to filter unwanted things or

object in a spatial domain or surface. In digital image

processing, mostly the images are affected by various noises.

The main objectives of the filters are to improve the quality of

image by enhancing is to improve interoperability of the

information present in the images for human visual. A general

structure of a filter mask is as follows.

Fig.1.1 Filtering Mask

Image filtering can be used for many aspects which

includes, smoothing, sharpening, noise eliminating and edge

detection etc. A filter is defined by a kernel, which

represented is a small array and applied to each pixel and its

neighbours within an image.

4.2 Frequency and Spatial Filters The frequency domain technique is based on the

convolution theorem. It decomposes an image from its spatial

domain form of brightness into frequency domain components

and is represented as the following equation �(�,�) =h( �,� )∗� (�,� ) ...... (1)

Where �(�,�) is the input image, h(�,�) is a position

invariant operator and �(�,�) is the resultant image from the

convolution theorem. �(�,�) = (�,�) (�,�) ........ (2)

Where G, H, F is the fourier transform of �, h, �

respectively. The transform H (u, v) is called transfer function

of the process. Here the edge in f(x,y) can be boosted by

using H(u,v) to emphasis the high frequency component of

F(u,v). In case of spatial filter works on pixels in the

neighbourhood of the pixel. The operation on sub image

pixels is defined using mask or filter with the same dimension.

4.3 Mean Filter (MF) The Mean Filter is a linear filter which uses a mask

over each pixel in the signal. Each of the components of the

pixels which fall under the mask are averaged together to form

a single pixel. This filter is also called as average filter. The

Mean Filter is poor in edge preserving. The Mean filter is

defined by:

Where M is the total number of pixels in the

neighborhood N. Mean filtering is a simple, intuitive and easy

to implement method of smoothing images, i.e. reducing the

amount of intensity variation between one pixel and the next.

It is often used to reduce noise in images.

The idea of mean filtering is simply to replace each

pixel value in an image with the mean value of its neighbours,

including itself. This has the effect of eliminating pixel values

which are unrepresentative of their surroundings. Mean

filtering is usually thought of as a convolution filter. Like

other convolutions it is based around a kernel, which

represents the shape and size of the neighbourhood to be

sampled when calculating the mean.

4.4 Wiener Filter The wiener filtering method requires the information

about the spectra of the noise and the original signal and it

works well only if the underlying signal is smooth. Wiener

method implements spatial smoothing and its model

complexity control correspond to choosing the window size

[9].

..... (4)

Where

H(u, v) = Degradation function

H*(u, v) = Complex conjugate of degradation function

Pn (u, v) = Power Spectral Density of Noise

Ps (u, v) = Power Spectral Density of un-degraded image

-1 -1 -1

-1 N -1

-1 -1 -1


245

Wiener filtering is able to achieve significant noise

removal when the variance of noise is low they cause blurring

and smoothening of the sharp edges of the image. Detection of

emotions in highly corrupted noisy environment this approach

involves removal of noise from the image by the Wiener Filter

for an automatic system for the recognition of facial

expressions is based on a representation of the expression

[10].

4.5 Laplacian Filter Laplacian is a 2-D isotropic measure of the second

spatial derivative of an image. The Laplacian of an image

highlights regions of rapid intensity change and is therefore

often used for edge detection. The laplacian is often applied to

an image that has first been smoothed with something

approximating a Gaussian smoothing filter in order to reduce

its sensitivity to noise. The operator normally takes a single

gray level image as input and produces another gray level

image as output.

V. PROPOSED METHODOLOGY

By considering the inefficiency of the existing image

enhancement methods there is a need to propose a new

methodology for face image enhancement which leads to

improve the quality of the image. A novel filtering technique

is proposed with the hybridization of Gaussian filter with

Median filter.

5.1 Gaussian filtering Gaussian filters are a class of linear smoothing filter

with the weights chosen according to the Gaussian functions.

Mainly these kind filters are used to smooth the image and to

eliminate the Gaussian noises.

πσσ σπσ

2 2

2 2h(m,n)=

1e

2

1 X

2 e22m n .... (5)

From the above equation 5 shows the Gaussian filter

is separable. The Gaussian smoothing filter is very good in

noise removal in normal distribution function. This filter is

rotationally symmetric the amount of smoothening is all

direction. The degree of smoothening is governed by variance

T.

5.2 Median Filtering

Then Median filter, the most prominently used

impulse noise removing filter, provides better removal of

impulse noise from corrupted images by replacing the

individual pixels of the image as the name suggests by the

median value of the gray level The median of a set of values is

such that half of its values in the set are below the median

value and half of them are above it and so is the most

acceptable value than any other image statistics value for

replacing the impulse corrupted pixel of a noisy image for if

there is an impulse in the set chosen to determine the median

it will strictly lie at the ends of the set and the chance of

identifying an impulse as a median to replace the image pixel

is very less. A commonly used non-linear operator is the

median, a special type of low-pass filter. The median filter

takes an area of an image (3x3, 5x5, 7x7, etc.), sorts out all the

pixel values in that area and replaces the center pixel with the

median value. The median filter does not require convolution.

The best known order-statistics filter is the median filter,

which replaces the value of a pixel by the median of the gray

levels in the neighborhood of that pixel,

The original value of the pixel is included in the

computation of the median. Median filters are quite popular

because, for certain types of random noise they provide

excellent noise reduction capabilities, with considerably less

blurring than linear smoothing filters of similar size.

Fig.1.2 Process flow of GMF Model

The above figure 1.2 shows the process flow of the

proposed preprocessing technique. Initially, the input image is

selected from the standard fingerprint database. Then the

Gaussian Median filter is applied on the input image for image

enhancement of Gaussian filter and Median filter to eliminate

the noise presents in the image. In order to enhance the clarity

of the image the median filter is applied with the amplification

factor A. Finally the preprocessed image has been obtained

with better quality.

VI. SIMILARITY MEASURES

6.1 Mean Squared Error (MSE) Mean square error is given by

[ ]

2

1 1

1( , ) ( , )

M N

i iMSE g i j f i j

MN = == −∑ ∑

.... (7)

Where M and N are the total number of pixels in the

horizontal and the vertical dimensions of image, g denotes the

Noise image and f denotes the filtered image.

6.2 Peak Signal to Noise Ratio (PSNR) The peak Signal to Noise ratio is calculated by:

2

10

25510 logPSNR

M SE

=

.... (8) For the image quality measures, if the value of the

PSNR is very high for an image of a particular noise type then

is best quality image.

Input Image

Preprocessed Image

Add Noise

Gaussian Filter

Median Filter


246

VII. ALGORITHM

I. PAGE SIZE

VIII. EXPERIMENTATION & RESULTS The performance of the existing filters is measured

by conducting the following procedure. The performance of

the filters is measured by applying noise on the face images.

The sample face images downloaded are used to analysis this

work. For our experiments, the sample facial images from the

standard ORL face image database are used. It contains a total

of 4000 images containing 40 subjects each with 10 images

that differ in poses, expressions and lighting conditions.

Figure 1.3 shows the sample images used in our experiments

used noise. From the figure 1.4 shows noise removed using

mean filter, wiener filter and proposed Gaussian Median filter. Original Image Noisy Image

Fig.1.3 Sample and Noisy image Mean Filter Wiener Filter

Input : Input image from IDB

Output : Preprocessed Image

Step 1: Read an image from the image database (IDB)

Step 2: Add Noise to an input image.

Step3: Apply Gaussian filter on the input image.

Step 4: Then apply Median filter on the input image.

Step 5: Calculate PSNR values.

Step 6: Repeat step 2 and step 4 for all images in

database (IDB).

Step 7: Stop


247

Laplacian Filter Gaussian Median Filter

Fig.1.4 Output image

Image

Id

Mean

Filter

Wiener

Filter

Laplacian

Filter

Gaussian

Median Filter

1 43.71 8.53 55.14 49.31

2 60.73 12.71 50.48 62.40

3 33.73 9.48 36.60 42.02

4 61.09 14.0 39.03 77.64

5 37.63 8.02 29.87 41.93

6 64.72 13.44 44.35 75.56

7 55.14 13.75 40.26 57.37

8 31.96 7.34 29.81 32.66

9 42.94 10.84 31.37 36.29

10 45.24 9.56 25.89 58.13

Table 1. MSE Comparison Values

From the table 1 shows the experimented values

obtained from different preprocessing methods. It shows the

selected face image from the database. The performance was

evaluated using the Mean Square Error (MSE) and Peak

Signal Noise Ratio (PSNR) in order evaluates the quality of

the image. By the analysis of the values in the table the

Gaussian Median filter is better with less MSE and high

PSNR values. In order to evaluate the performance of the

Gaussian Median filter considered the obtained results with

the existing Mean filter, Wiener filter and laplacian filter are

shown in the following table 2.

Image

Id

Mean

Filter

Wiener

Filter

High Boost

Filter

Gaussian

Median Filter

1 31.75 28.85 30.78 35.31

2 30.33 27.12 29.26 33.06

3 32.88 28.39 27.55 37.37

4 30.30 26.69 28.47 33.75

5 32.40 29.12 30.10 37.40

6 30.05 26.87 29.79 34.09

7 30.74 26.78 28.05 33.79

8 33.11 29.50 27.69 37.50

9 31.83 27.81 28.12 36.05

10 31.60 28.35 29.33 35.58

Table 2. PSNR Comparison Values


248

From the below figure 4 shows the pictorial

representation of the performance evaluated. By analysing the

obtained results the proposed model produced the best results.

Hence the Gaussian Median filter is an efficient one.

IX. CONCLUSION In this paper, the noise removal of images based on

Gaussian Median filtering has been presented. The

experimental result proves the effectiveness of this approach,

providing good PSNR values when compared to existing

methods. The performances of PSNR values of proposed

Gaussian Median filtering when compared to existing

methods Mean filter, Wiener Filter and Laplacian Filter are

investigated independently. The proposed Gaussian Median

filtering produces better results with 35.39% accuracy

compared with existing methods gives 30.05 % accuracy for

Mean filter, Wiener Filter with 27.94% accuracy and laplacian

Filter with 29.26% accuracy. Moreover, the computational

cost of the algorithm is very low. Therefore, the proposed

algorithm candidates itself for implementation is in simple

low-cost cameras or in video capture devices with high values

of resolution and frame rate. The proposed scheme is capable

of achieving at least comparable and often better performance

than existing iterative filtering techniques.

REFERENCES [1] L. Beaurepaire, K.Chehdi, B.Vozel, ‘Identification of

the nature of the noise and estimation of its statistical

parameters by analysis of local histograms’,

Proceedings of ICASSP-97, Munich, 1997.

[2] Noise Models, http:// home pages.

inf.ed.ac.uk/rbf/CV online/ LOCAL _COPIES

[3] Image Noise, http://en. wikipedia.org/wiki/Image_

Noise.

[4] H.S.M. Al-Khaffaf , A.Z. Talib, R. Abdul Salam, ‘A

Study on the effects of noise level, cleaning method,

and vectorization software on the quality of vector

data’, Lecture Notes in ComputerScience 299-309.

[5] D.Gnanadurai, and V.Sadasivam, “An Efficient

Adaptive Thresholding Technique for Wavelet based

Image Denoising”, International Journal of Signal

Processing, Vol.2, pp.115-128, no.2, 2003.

[6] Chan, Chung Wa Ho, and Mila Nikoloya, “Salt-

andPepper Noise Removal by Median-type Noise

Detectors and Detail-preserving Regularization”, Image

Processing, IEEE Transactions. Volume 14, pp: 1479-

1485, Issue 10, Oct.2005.

[7] C.Lakshmi& M.Sundararajan, “Biometric Security

system using Face Recognition”, Publication of

International Journal of Pattern Recognition and

Research, pp. 125-134, July 2009.

[8] Jappreet Kaur, Manpreet Kaur, Poonamdeep Kaur,

Manpreet Kaur,” Comparative Analysis of Image

Denoising Techniques”, International Journal of

Emerging Technology and Advanced Engineering,

ISSN 2250-2459, Volume2, Issue 6, June 2012.

[9] Anjali Malviya,” Fractal Based Spatial Domain

Techniques for Image De-Noising”.

[10] Kanchan Lata Kashyap et.al. “Noise Removal of Facial

Expression Images using Wiener Filter”, National

Conference on Emerging Trends in Computer Science

and Information Technology (ETCSIT) 2011

Proceedings published in International Journal of

Computer Applications.

@IJITCE Publication

ijitce nov2014

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