Performance of Gabor Mean Feature Extraction Techniques for Ear Biometrics Recognition

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Copyright © IJIFR 2015
Original Paper
InternationalJournalofInformative&FuturisticResearch ISSN (Online): 2347-1697
Volume 2 Issue 6 February 2015
Abstract
Authentication of a person's identity is a very old but challenging problem.
There are three common ways which are used for authentication. First one
is based on what a person has (Possession) such as keys, identity cards etc.
Second mode of authentication is based on what a person knows or
remembers (Knowledge) such as passwords, PINs etc. Third way of
authentication is based on what a person carries, i.e. the characteristics of
a human being (Biometrics). Biometrics is the science of establishing
human identity by using physical or behavioural traits such as face,
fingerprints, palm prints, iris, hand geometry, ear, and voice. There are
some significant works which have been carried out in past few years in
the field of ear biometrics. In this survey discusses various technique of ear
Detection & Recognition for 2D ear biometric, and provides good future
prospects for the upcoming researchers in the field of ear biometric.
1. Introduction
The prime reasons behind their inclination are due to the presence of all the properties i.e.
universality, uniqueness, permanence and collectable in nature in ear biometric. The purpose of
using a biometrics is to provide a mechanism to recognize a person with the help of his/her
biological characteristics and to eliminate the use of much inconvenient ways of recognition which
are based on ID card, password, physical keys etc. First discovered by the French criminologist
Review of Detection & Recognition
Techniques for 2D Ear Biometrics System
Paper ID IJIFR/ V2/ E6/ 072 Page No. 1888-1898 Subject Area
Computer Science
& Engineering
Key Words Ear Databases, Ear Detection Technique, Ear Recognition Technique
Shritosh Kumar 1
M.Tech. Scholar
Department Of Computer Science & Engineering
Arya College of Engineering & IT, Jaipur, India
Prof. Vishal Shrivastav 2
Professor
Department Of Computer Science & Engineering
Arya College of Engineering & IT, Jaipur, India

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ISSN (Online): 2347-1697
International Journal of Informative & Futuristic Research (IJIFR)
Volume - 2, Issue - 6, February 2015
18th Edition, Page No: 1888-1898
Shritosh Kumar, Prof. Vishal Shrivastav :: Review of Detection &
Recognition Techniques for 2D Ear Biometrics System
Bertillon, and refined by the American police officer Iannarelli, who advocated that the shape and
appearance of the outer ear for humans is unique, and relatively unchanged throughout the lifetime
of an individual [2]. First ear recognition system based on only seven features. Ear recognition
consists of two important steps:
a. Ear detection that carries out the segmentation of the ear from profile face before using it
for recognition task.
b. Ear Recognition for correct recognition Feature Selection and feature optimization stage
has an important role for accuracy.
1.1 Biometric Properties
The requirements of a biometric system and has suggested the following properties that a biometric
characteristic/trait should possess to make itself suitable for successful authentication by Roger
Clarke [3].
 Universality: Every person should have the biometric characteristic and it should seldom
lose to an accident or disease.
 Uniqueness: No two persons should have the same value of the biometric characteristic
i.e. it should be distinct across individuals.
 Permanence: Biometric characteristic should not change with time. It should not subject
to considerable changes based on age or disease.
 Collectability: Biometric characteristic should be collectable from anyone on any
occasion.
 Acceptability: Society and general public should have no objection to provide the
biometric characteristic.
 Measurability: Measurability is meant for the possibility of acquiring and digitizing the
biometric characteristic using some suitable digital devices/sensors without causing any
inconvenience to the person.
 Circumvention: A biometric characteristic can be imitated or forged. By circumvention it
is meant that the system should be able to handle these situations effectively.
1.2 Anatomy of Human Ear
1. Crura(antihelix), 2. Crus(helix), 3. Anterior
notch, 4. Supratragal tubercle, 5. Traqus, 6.
Intertragal notch, 7. lobule, 8. external
auditory meatus, 9. antitragus, 10. posterior
auricular sulcus, 11. antihelix, 12. cayum, 13.
cymba, 14. scaphoid fossa, 15. Darwinian
tubercle, 16. Triangular fossa, 17. helix
Figure 1.2.1: Anatomy of Human Ear
2. Ear Recognition Process
The basic process of ear recognition includes following stages:-
i) Image Acquisition: The ear acquisition phase uses ear image from several different
conditions. But Images used for ear recognition are static images or image sequences.

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ii) Pre-processing: According to need the following pre-processing steps may be
implemented in an ear detection system.
a) Resizing
b) Cropping
c) Frequency Histogram
iii) Feature Extraction: feature extraction is a special form of dimensionality reduction.
iv) Filtering or Feature Reduction
v) Classification: with the help of a pattern classifier, extracted features of the ear image is
compared with the ones stored in an ear library and to determine the ear for a given feature
vector.
vi) Result
3. Available Databases For Ear Biometrics
In order to Test and development of robust ear recognition algorithms require databases of
sufficient size. Many university and organization provides a collection of ear images databases. In
this section, we review several databases that have been used in the literature of ear recognition
(and detection) which can either be downloaded freely or can be licensed with reasonable effort.
3.1 IIT Delhi Ear Database
The IIT Delhi ear image database consists of the ear image database collected from the students
and staff at IIT Delhi, New Delhi, India. The currently available database is acquired from the 121
different subjects and each subject has at least three ear images. All the images are acquired from a
distance (touchless) using simple imaging setup and the imaging is performed in the indoor
environment. The resolution of these images is 272 * 204 pixels and all these images are available
in jpeg format.The database of 471 images has been sequentially numbered for every user with an
integer identification/number. The database can be freely downloaded from the given URL.
http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Ear.htm
3.2 IIT Kanpur Ear Database
IIT Kanpur ear database composed of two data sets. Data Set 1 contains 801 side face images
collected from 190 subjects. Number of images acquired from an individual varies from 2 to 10.
Images of Data Set 2 consist of frontal view of the ears captured at three positions, first when
person is looking straight, second when he/she is looking approximately 200
down and third when
he/she is looking approximately 200 up with resolution 640 * 480 pixels and all these images are
available in jpeg format. These images are captured using a digital camera from a distance of 0:5 to
1 meter.
http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Ear.htm.
3.3 AMI Ear Database
AMI Ear Database was created by Esther Gonzalez. It consists of ear images collected
from students, teachers and staff of the Computer Science department at Universidad de Las
Palmas de Gran Canaria (ULPGC), Las Palmas, Spain. The database was acquired from 100
different subjects, For each individual, seven images (six right ear images and one left ear image)
were taken. Five of the captured images were right side profile (right ear) with the individual

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facing forward (FRONT), looking up and down (UP, DOWN) and looking left and right (LEFT,
RIGHT). The sixth image of right profile was taken with the subject also facing forward but with a
different camera focal lenght (ZOOM). Last image (BACK) was a left side profile (left ear). The
database of 700 images has been sequentially numbered for every subject with an integer
identification number. The resolution of these images is 492 x 702 pixels and all these images are
available in jpeg format. The database can be freely downloaded from the given URL.
http://www.ctim.es/research_works/ami_ear_database/subset-1.zip(subset-1-4.zip)
Figure 3.3.1: Sample Image from AMI Database
3.4 USTB Ear Database
The USTB Ear Database consists of the ear image database collected from the Students and
teachers from the department of Information Engineering, USTB. All images are right side profile
full images. The distance between camera and subject is 1.5 meters. The resolution of image is
768*576, 24-bit true color. Define the angle when CCD camera is perpendicular to ear as 0 degree,
which we call profile side. Right rotation: Under this condition, the subject rotates from 0 degree to
60 degree with variable intervals to the right side. Specifically, the angles are 0 degree, 5 degree,
10 degree, 15 degree, 20 degree, 25 degree, 30 degree, 40 degree, 45 degree and 60 degree. Each
angle is photographed two images and thus the sum of images is 22, labeled by 1-22. Left rotation:
It is the same with the condition of turning right but it only rotates to 45 degrees. Therefore, it only
contains 18 images, labeled by 1-18. The database can be freely downloaded from the given URL.
http://www1.ustb.edu.cn/resb/en/subject/subject.htm
Figure 3.4.1: Sample Image from USTB Database
3.5 UND Ear Database
The University of Notre Dame (UND) ear databases are available free of cost. Side profile images
of a subject taken at arbitrary angles and under different lighting conditions. There are several
collections for various modalities. 464 different visible-light face side profile (ear) images from
114 human subjects with arbitrary number of images per subject Size of each image is 1600x1200.
These images are used for testing, so the original images are directly input to the detector without
any scaling. The database can be freely downloaded from the given URL.
http://cbl.uh.edu/URxD/datasets/
3.6 UBEAR Ear Database
UBEAR Ear Database contains images from the left and the right ear of 126 subjects, for each
individual, four images, and the subjects were not asked to remove hair, jewelry or headdresses
before taking the pictures. The images are cropped from video stream, which shows the subject in
different poses, such as looking towards the camera, upwards or downwards. The database of 430

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images has been sequentially numbered for every subject with an integer identification number.
The resolution of these images is 1280 x 960 pixels and all these images are available in TIFF
format. The database can be freely downloaded from the given URL.
http://ubear.di.ubi.pt/ubear.html
3.7 WPUT-Database
The West Pommeranian University of Technology has collected an ear database with the goal of
providing more representative data than comparable collections. The database contains 501
subjects of all ages and 2071 images in total. For each subject, the database contains between 4 and
8 images, which were taken on different days and under different lighting conditions. The subjects
are also wearing headdresses, earrings and hearing aids, and in addition to this, some ears are
occluded by hair. The database can be freely downloaded from the given URL.
http://ksm.wi.zut.edu.pl/wputedb/
Figure 3.7.1. Sample Image from WPUT Database
3.8 NCKU Database
The National Cheng Kung University in Taiwan has collected an image database, which consists of
37 images for each of the 90 subjects. Each subject is photographed in different angles between 0-
90 degrees in 5 degree steps. The database can be freely downloaded from the given URL.
http://robotics.csie.ncku.edu.tw/Databases/FaceDetect_PoseEstimate.htm#Our_Database_.
3.9 YSU Database
The Youngston State University collected a new kind of biometric database for evaluation forensic
identification systems. The images are grabbed from a video stream and show the subject in poses
between 0-90 degrees. For each of the 259 subjects, 10 images are provided. This means that the
database contains right profile images and a frontal view image for each subject.
3.10 XM2VTS Database
XM2VTS database: The selected dataset consists of 252 images from 63 individuals with four
images per person collected during four different sessions over a period of five months to ensure
the natural variation between the images of the same person. At each session two head rotation
shots and six speech shots were recorded. The ears in the database are not occluded by hair but
there are few images with some occlusion by earrings. The images selected are those where the
whole ear is visible in a 720×576 24-bit image. The database can be freely downloaded from the
given URL.
http://www.ee.surrey.ac.uk/CVSSP/xm2vtsdb/.
Figure 3.10.1: Sample Image from XM2VTS Database

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3.11 SOTON Ear Database
Two datasets are selected from the SOTON ear database. Dataset 1 contains 548 face profile
images from 137 subjects with four images per person. Dataset 2 is selected to enable analysis to
be performed over time which is captured in five different sessions over a period of eleven months.
The database is acquired as subjects walk past a camera triggered by a light beam signal, and other
biometrics are acquired at the same time. This database therefore allows evaluation of the
performance of our technique on a data acquired in a more realistic scenario. The advantage of this
database is that, it has much variation in ear orientation, size, color skin, and lighting condition.
3.12 WVU Database
The West Virginia University (WVU) ear database was collected face images of a user which vary
from side profile to frontal profile in small increments. The currently available database is acquired
from the 120 subjects with 10 images per subject. The original images are of size 480x640 and the
cropped ear images have variable sizes. Typical average cropped ear is of size 128x94: these
images are used for training, so images scaled to 24x16. The database can be freely downloaded
from the given URL.https://lib.wvu.edu/databases/
3.13 Magna Database
Magna database was collected face Images taken from two different sensors and five different
profiles. The currently available database is acquired from the 3360 subjects with 8 images per
subject with different profiles. The size of each image is 1200x1600. These images are used for
testing: so the original images are directly used without any scaling.
3.14 UCR Database
The University of California Riverside (UCR) database was assembled from images captured by
the Minolta Vivid 300 camera. The range image contains 200 × 200 grid points. The database
contains 902 shots for 155 subjects. Each subject has at least four shots. There are 17 females; six
subjects have earrings and 12 subjects have their ears partially occluded by hair (with less than
10% occlusion). The UCR database is currently not available to the public.
3.15 NIST Mugshot Identification Database
The NIST Mugshot Identification special database contains both front and side (profile) views.
Profiles have 89 cases with two or more profiles and 1268 with only one profile. Cases with both
fronts and profiles have 89 cases with two or more of both fronts and profiles, 27 with two or more
fronts and one profile, and 1217 with only one front and one profile. Separating front views and
profiles, there are 131 cases with two or more front views and 1418 with only one front view. The
database can be freely downloaded from the given URL.
http://www.nist.gov/srd/nistsd18.cfm.
4. Techniques Used For Ear Detection
Ear detection (segmentation) is an essential step for automated ear recognition systems, though
many of the published recognition approaches achieve this manually.[4] However, there have been
several approaches aimed at providing fully automated ear detection. This section describes some
of the semi-automated (computer-assisted) and automated techniques.

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4.1 Computer-Assisted Ear Segmentation
Computer-Assisted Ear Segmentation is semi-automated methods require user-defined landmarks
specified on an image, a two-line landmark, with one line along the border between the ear and the
face, and the other from the top of the ear to the bottom, in order to detect the ear region. This
method proposed by Yan and Bowyer [2005a] [5].
4.2 Template Matching Techniques
Ear image using deformable contours on a Gaussian pyramid representation of the image gradient.
Then edge relaxation is used to form larger curve segments, after which the remaining small curve
segments are removed by using of canny operator. This method proposed by Burge and Burger
[2000] [6].
4.3 Shape Based
By the use of Hough Transform (HT) the elliptical shape of the ear find proposed by ArbabZavar
and Nixon [2007] [7].
4.4 Morphological Operators Techniques
We found fully automated ear segmentation scheme by employing morphological operators, but
have sum problem to solved it by the used low computational cost appearance-based features for
segmentation, and a learning-based Bayesian classifier for determining whether the output of the
segmentation is incorrect or not. This solution addressed by HajSaid et al. [2008] [8]
4.5 Hybrid Techniques
By the used of Principal Component Analysis (PCA) introduced the notion of “jet space similarity"
for ear detection, which denotes the similarity between Gabor jets and reconstructed jets.
Introduced by Watabe et al. [2008] [9].
For automatic ear detection used skin color and template-based technique for a side profile face
image Prakash et al. [2009] [10].
The image ray transform, based upon an analogy to light rays, to detect ears in an image. By
exploiting the elliptical shape of the helix, this method was used to segment the ear region. This
transformation is capable of highlighting tubular structures such as the helix of the ear and
spectacle frames. This method proposed by Cummings et al. [2010] [11].
4.6 Haar Based
The very fast and relatively robust face detection technique is in the domain of face detection as
the Viola-Jones method [Viola and Jones 2004][12].
A cascaded Adaboost technique based on Haar features for ear detection. Islam et al. [2008b]
trained the Adaboost classifier to detect the ear region, even in the presence of occlusions and
degradation in image quality. They reported a 100% detection performance [13].
5. Techniques Used For Ear Recognition
Basically researcher focuses on finding the methods for extracting features point present in the
subject image. Mark Burge and Wilhelm Burger reported the first attempt to automate the ear

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recognition process in 1997. They used a mathematical graph model to represent and match the
curves and edges in a 2D ear image.
Table 5.1: Different Techniques Used For Ear Recognition
S. No Technique Publication Database Perf.
1 Intensity Based
Chang Et Al. [2003] [14]. UND 72.7
Xie And Mu [2008] [15]. USTB IV
>80
2
Edge
detection
based
K. V. Joshi [2011] [16]. CVL 83
3
Force Field
Dong And Mu[2008] [17] USTB 75.3
4
Wavelet Transformation Arbab-Zavar, B. &
Nixon, M.S. [2011] [18].
USTB II 68.9
5 Geometrical Method
S. Bhalchandra
[2011][19].
- 85
6
Local
Descriptors
SIFT Kisku D. R. [2009] [20]. UND 78.8
LBP Xie and Mu [2008] [15]. USTB II 82
7 Gabor Filters
ICA Zhang H [2008] [21]. USTB 85
PCA
Chang et al. [2003] [22]. Human ID
71.6
LDA
Yuan and Mu[2007] [23] - 86.76
SFFS
Nanni and Lumini [2009]
[24].
UND 80
KFDA Ajay Kumar [2011] [25]. USTB I 83.97
SVM Yaqubi et al. [2008] [26]. USTB 75.
SRC Wright et al. [2009] [27]. UND 80
NSRC
Baoqing Zhang [2014]
[28].
UND J2 90
LRT Tak-ShingT. [2012] [29]. UND 84.84
GMM
Dakshina Ranjan Kisku
[2009] [30]
IIT Kanpur 93.35
LGPDP
Guo Y., Xu,
[2008][31].
USTB 89.5
NNA Banafshe [32] XM2VTS 85.7
And many more techniques used for ear recognition. In this paper we focus on Gabor Filter ear
recognition techniques.
6. Open Research Challenges And Future Applications
The face is not visible from a frontal angle, the ear can serve as a valuable additional characteristic
in these scenarios. This fact poses serious problems to biometric systems, using facial features for
identification.

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 Occlusion and Pose Variations
When the ear is partially occluded by hair or by other items such as headdresses, hearing aids,
jewelry or headphones, then methods for ear recognition can be severely impacted. Because of the
convex surface of the outer ear, parts of it may also be occluded if the subject's pose changes.
Figure 6. 1: Typical Example of occluded ears
 Ear Symmetry and Ageing
Because ear recognition is one of the newer fields of biometric research, the symmetry of the left
and the right ear has not been fully understood yet. The right ear of the subject was used as the
gallery and the left ear was used as the probe. They concluded that most people’s left and right ears
are symmetric to a good extent, but that some people’s left and right ears have different shapes.
 Different shape of ear
We have four different outer shapes of ear are:
A. Triangular
B. Round
C. Oval
D. Rectangular
Triangular Round Oval Rectangular
Figure 6.2: Example of Different shape of ear
 Image conditions:
When image is formed, factors such as lighting (source distribution, intensity and spectra) and
camera characteristics (lenses, sensor response) affect the appearance of an ear.
 Recognition Rate:
A wide research has been done in Ear Recognition by solving the research issues of ear
recognition under different illuminations, orientations and many other variations but no technique
has 100% correct recognition rate.
7. Conclusion
For ear biometrics we have more than 30 ear database Available. Few of ear database detail given
in this paper. From our point of view AMI ear database is good for our research. In this paper we
discuss different techniques used for ear detection. From our point of view Haar Based (Adaboost)

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technique is best for automated ear detection technique. We discuss different techniques used for
ear recognition. A Gabor Filter ear recognition technique is best for ear biometrics.The research
issues are to improve recognition rate by improving the pre-processing of datasets, improving the
feature extraction method and using the best classifier. In which Feature Extraction is key step on
which the generalization error is dependent. There are so many feature extraction technique
developed through research but no one feature selection have ability for extracting all the unique
feature of an ear biometric image. So there is a need of optimized feature extraction technique
which can increases the recognition rate in ear biometric analysis.
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 SIFT -Scale Invariant Feature Transform
 PCA - Principal Component Analysis
 SVM -Support Vector Machine
 ICA -Independent Component Analysis
 LDA -linear discriminant analysis
 SFFS -Sequential Forward Floating Selection,
 KFDA - kernel Fisher Discriminant Analysis
 LBP-Local Binary Pattern
 LRT - local Radon transform
 NSRC - nonnegative sparse representation classification
 SRC - sparse representation classification
 GMM - Gaussian Mixture Model
 LGPDP - local Gabor phase difference pattern

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