sneha. g.gondane

8/2/2019 Sneha. G.Gondane

1/12

Classifier Feature Extraction Techniques for Face

Recognition System under Variable Illumination

Conditions

Sneha G Gondane1, Dhivya M*1 PG Student ([email protected]), *2 Research Scholar ([email protected])

Department of Electrical and Electronics Engineering,

Anna University of Technology, Coimbatore-641047

Tamilnadu

Abstract. Recognition of object under uncontrolled illumination environment is

imprecise and vague. A simple image preprocessing chain is taken for precept.

Local binary pattern (LBP) is capable of reducing noise levels in background

regions. Local ternary patterns (LTP) fragmenting less under noise in uniform

regions. Gabor filter acts as a resounding filtering source for local spatial

frequencies. Phase congruency is to extract the image in phase as well as in

magnitude levels. The result is obtained by the KLDA based classifiers with

combination of LBP and Gabor features. The above explained features are

obtained from both the input and the data base image. In that the LBP andGabor features are fused and the distance is calculated. If both the input and

database images are same, the face is recognized; otherwise the face is not

recognized. The simulation results exemplify the proposed technique for image

with different lighting, expressions and structural defects.

Keywords: Face recognition, lightning invariance, local binary patterns, local

ternary pattern, phase congruency.

1 Introduction

FACE recognition has created immense opportunity in the field of science and

technology to establish a well set platform among the basic standards in this

technological world. Computer analysis does not recognize various techniques which

can only extract and recognize its features. Face image recognition acquired in its

surrounding environment with changes in lighting or pose remains a largely
mailto:[email protected]:[email protected]:[email protected]:[email protected]


2/12

unanswered dilemma. Most of these methods were initially developed with face

images collected under relatively well-controlled conditions. In practice, they have

complexity in dealing with the range of appearance varying from certain approaches

that can commonly occur in unconstrained situation due to illumination levels, pose,

facial expressions, aging of networks and partial occlusions. This paper focuses

mainly on the issue of sturdiness to illumination variations. A face corroboration

system capable of implementing images for a portable device should be able to verify

a client at any time.

2 Existing System

Recognition system models the structural patterns of an imaging process under

difficult lightning conditions. Patterns are being processed in various stages according

to their level of intensity. Specifically three main contributions are described in

previous works. First Efficient preprocessing chain needed before any feature

extraction techniques is widely carried on. Local binary pattern (LBP) improves the

face identification and gabor wavelets which is used to filter the noise levels.

Improve sturdiness by adding Kernel principal component analysis (PCA) feature

extraction during later stages of analysis.The result is obtained by the KPCA basedclassifiers. The above explained features are obtained from both the input and the data

base image. If both the input and database images are exactly same, then the face is

recognized otherwise the face is not recognized. The image with difficult lighting,

same expressions can be identified.

2.1. Existing System Flow Diagram

2


3/12

Fig. 1. Flowchart of existing system.

2.1.1. Pre processing

Pre processing is an important stage of face detection system. It is better to present

a simple and efficient preprocessing chain which eliminates the structural effects of

illumination while still preserving the necessary details that are needed for

recognition. Preprocessing stage includes: Gamma correction, Difference of Gaussianfiltering, Masking and Contrast Equalization.

Fig. 2. Various stages of preprocessing: input image; Gamma corrected image; image after

DoG filtering; image after contrastnormalization.

2.1.1.1 Gamma Correction

Gamma Correctionimproves the structural formation of arrays which is under

the region of darkness. Shading effects will not undergo any changes during its

illumination stages. Data images are compressed in bright regions and improve the


4/12

quality level of images being processed. Gamma correction transforms the gray

level into object level based on the intensity of light being reflected on the data

images.

2.1.1.2 Difference of Gaussian (DOG) Filtering

DOG will suppress the highest spatial frequencies based on the reduction level

of both aliasing and noise signals. Decaying of the structural images causes

simplification of internal progression of the filtering steps further more. High-pass

filtering removes both useful and source information gathered during the structural

progression. Level of analysis during simplification process will greatly influencethe overall system performance.

2.1.1.3 Masking

Masking is the track of achieving better imaging process in the facial regions

(hair style, beard) that are felt to be unconditional or source variables which are

needed to be masked should be applied at this point. Masking is the optional

process during analysis.

2.1.1.4 Contrast Equalization

The final stage of our preprocessing chain rescales the image intensities to

standardize a robust measure of overall contrast or intensity variation. It is

important to use a global estimator because the signal indication level typically

contains extreme values produced by highlighters, small dark regions such as

nostrils, garbage at the image borders, etc.

2.1.2. Local Binary Patterns

LBP method provides very good results, both in terms of speed as well as

discrimination performance. Because of the way the texture and shape of images is

described, the method seems to be quite robust against face images with different

facial expressions under different lightening conditions, image rotation and aging of

4


5/12

persons. The middle pixel value is used as a threshold frequency which compares

with the eight neighboring channels of a pixel.

Fig. 3. Illustration of the basic LBP operator.

If a neighbor pixel has a higher gray value than the center pixel, then one is

assigned to that pixel,else it gets a zero.

2.1.3 Gabor Features

Gabor filter suits the certain level of frequencies which allows a particular band

to pass and helps in local spatial frequency distribution. Images are optimally

oriented in each part of the object with equal frequency and spatial domains. Gabor

feature also encodes the face over broader range of scales. The Gabor feature is a

frequency based solution technique under the region of sinusoidal plane wave and

orientation phase.

2.1.4 Kernel Principle Component Analysis

Kernel principal component analysis is a combination of local binary patterns

and Gabor feature through which the distance is calculated. Using a kernel, the

original linear operations of PCA are done by reproducing Kernel Hilbert Space with

a non-linear mapping.

3 Proposed System

Face recognition system has created advance level of technology in the field of

medical science. Everyday new advancement from the previous stages is carried out

for the welfare of the society. When compared to previous works, solution with phase

congruency provides precise and accurate results. Existing system is the base for our

proposed system. Various stages are being carried out based on the existing system.


6/12

Local binary patterns and local ternary patterns are being processed with the Gabor

filters in order to reduce the noise level in unconditional regions. Gabor filter gives an

optimal solution for structural analysis in spatial and frequency domains. Phase

congruency is to extract the images in phase as well as in the magnitude levels. The

kernel linear discriminant analysis is used to extract the feature using combination of

local binary patterns and Gabor features through which distance is calculated .The

input image and data base image compared and face is recognized.

3.1. Flowchart for feature extraction techniques

Fig. 4. Flow chart of feature extraction.

3.1.1 Local Ternary Pattern

Local ternary patterns reduce the illumination effects. In this we itself choose a

threshold value and compare with the neighbor pixels value. If the central value is

greater than neighbor pixel value, then it will show as 1. If the central value is in

between than neighbor pixel value, then it will show as 0. If the central value is lesser

than neighbor pixel value, then it will show as 0.

6


7/12

Fig. 5. Illustration of the basic LTP operator.

3.1.2 Phase Congruency Features

Gradient-based operators who search for their effective approaches look under themaximum intensity gradient. Intensity gradient will undergo some structural failures

during detection and localize a large proportion of features within images. Phase

congruency is used to identify and analyze the corners and edges from the structural

images. Unlike the edge detectors, which identify the sharp changes from the

database congruency model to detect points of order in the phase spectrum. Phase

component is the most important part of phase congruency rather than its magnitude.

4 Simulation Results and Discussion:

The simulations are carried out in MATLAB (7.11.0.584). A detailed survey and

analysis of previous works is carried out and the simulation parameters are chosen.

The Input Image sequence and the Data base image sequence are listed in figure 6 (a)

and 6(b) respectively. In table 1, the comparison results are given. It is evident that

the proposed technique is less time consuming/time constraint. The classifier

extraction technique requires nearly less than half the time consumed by the previous

work.

Table 1: Comparison

Parameter Existing System Proposed System

Time

Recognition

Techniques

40.2015 sec

The image with difficult lighting,

same expressions can be identified

Local binary patterns, Gobar

16.1929 sec

The image is

recognized with

different lightning,

facial expressions, any

spot or scratches.


8/12

features, kernel principle component

analysis Local ternary

patterns, phase

congruency

Fig. 6 (a). Input images.

Fig. 6 (b). Data base images

Fig. 7. Face Recognition under blurred image.

8


9/12

Fig. 8. Face Recognition under darker lightning

In Fig 7,the input and data base image is recognize under difficult lightning

conditions when the images are same. In Fig 8, the input and data base image is

recognize when the input image is selected under dark lightning.The input image

passes through various feature extraction tecniques , compare with data base image

and recognize the image.

Fig. 9. Face recognition under different expressions.


10/12

Fig. 10. Face Recognition under structural defects.

Fig 9 In this figure the input and data base image is recognize when the input

image with expressions under difficult illumination is selected. The input image

passes through various feature extraction tecniques , compare with data base image

and recognize the image.Fig 10:In this figure the input and data base image isrecognize when the input image with scratches under difficult illumination is selected.

The input image passes through various feature extraction tecniques , compare with

data base image and recognize the image. Fig 11:In this figure if the input image is

selected which is not present in data base image than after comparing input and data

base image the image will not recognize.

10


11/12

Fig. 11. Face Recognition out of data base.

5 Conclusion

In this paper a classifier based feature extraction technique is implemented for face

recognition under uncontrolled illumination with different lightning conditions. The

result is obtained by the KLDA based classifiers with combination of LBP and Gaborfeatures. Local ternary patterns reduce noise level since using the own threshold value

makes the feature to extract and recognize the face, precisely. Phase congruency can

map 0 to 360 degrees to 0 to 255 gray values, which help to extract the minute

features and is applicable in phase and time domains. Time consumption is lesser in

proposed method compared to the previous methods. From the simulated image

results the efficacy of the classifier technique is hence shown.

References

1. Xiaoyang Tan and Bill Triggs Enhanced Local Texture Feature Sets for FaceRecognition under Difficult Lighting Conditions IEEE transactions on imageprocessing, vol. 19, no. 6, June 2010.

2. Peter Kovesi Phase Congruency Detects Corners and Edges School of ComputerScience & Software Engineering The University of Western Australia Crawley, W.A.6009 [email protected] .

3. W. Zhao, R. Chellappa, P.J.Phillips, A. Rosenfeld Face Recognition: A LiteratureSurvey ACM computing surveys, vol. 35, no. 4, December 2003, pp. 399458.
mailto:[email protected]:[email protected]


12/12

4. Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman Eigenfaces vs.

Fisherfaces: Recognition using Class Specific Linear Projection IEEE transactions onpattern analysis and machine intelligence, vol. 19, no. 7, July 1997 .

5. Kernel principal component analysis" http:// en.wikipedia.org / wiki/ Kernel,analysis from Wikipedia, the free encyclopedia.

6. Malik, J. Sainarayanan, G. Dahiya, R Corner Detection using Phase CongruencyFeatures Signal and Image Processing(ICSIP), 2010 International Conference on15-

17 Dec. 2010.

7. Alvy Ray Smith Gamma Correction Technical Memo 9 September 1, 1995.8. Sneha G Gondane Recognition and Detection System in Human Face Under Variable

Illumination Conditions Amrita School of Engineering.Third national conference on

recent trends in computation and signal held on 1 march 2011.

9. RajGupta,Harshal Patiland Anurag MittalRobust Order-based Methods for FeatureDescription Department of Computer Science and Engg. Indian Institute ofTechnology Madras,Chennai INDIA-600036.

10.Nagachetan Bangalore, Rupert Young, Philip Birch, Chris Chatwin Tracking MovingObjects Using Band pass Filter Enhanced Localization and Automated Initialization of

Active Contour Snakes Industrial Informatics Research group, Department ofEngineering and Design, University Of Sussex, Brighton, BN1 9QT, UK.

11.Weili Ding Xiaoli Li Wenfeng Wang Robust Image Corner Detection Using LocalLine Detector and Phase Congruency

Model Inst. of Electr. Eng., Yanshan Univ., Qinhuangdao, China InformationEngineering (ICIE), 2010 WASE International conference.

12.Ahmadian, A. Mostafa, A An efficient texture classification algorithm using Gaborwavelet Dept. ofBiomedica System & Medical Phys., Tehran Univ. of Medical Sci.,

Iran Engineering in Medicine and Biology Society, 2003Issue Date17-21 Sept. 2003,

Volume 1, Date of Current Version : 05 April 2004.

13.Chunyu Zhang Keyou Guo Guizhen Yu An Analysis of Gabor Wavelet Algorithm forTracking Driver's FeaturePointKey Lab. of Intell. Transp. Syst. Technol., Nat. Center of ITS Eng. & Technol.,

Beijing, China. Electrical and Control Engineering (ICECE), 2010 InternationalConference. November 2010.

14. Ekenel, H.K. Fischer, M. Tekeli, E. Stiefelhagen, R. Ercil A Local Binary PatternDomain Local Appearance Face Recognition Signal Processing, Communication andApplications Conference, 2008.

15. Rodrigo Verschae, Javier Ruiz-del-Solar and Mauricio Correa Face Recognitioninon Unconstrained Environments:A Comparative Study Department of ElectricalEngineering,Universidad de Chile.

12
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5686259http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5686259http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5686259http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570019http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570019http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570019http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570019http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5686259http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570019http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5570019

sneha. g.gondane

Documents