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8/2/2019 Ear Bio Metrics 090915041404 Phpapp01

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Institute of Telecommunication

Image Processing Group

Ear Biometrics for HumanIdentification Based on Image Analysis

Michal ChorasImage Processing Group


ATR Bydgoszcz, Poland

Presentation for ELCVIA Journal


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Micha Chora-Ear Biometrics for Human Identification

INTRODUCTION TO HUMAN IDENTIFICATION

Traditional methods:

PINs

Logins & Passwords Identification Cards

Specific Keys

Disadvantages of the

traditional methods:

hard to remember

easy to loose

lack of security

cards and keys are

often stolen

passwords can be

cracked

invasivenessIdentification by something

that people know or possess.


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INTRODUCTION TO BIOMETRICS

Definition: automatic identification of a living person based on

physiological or behavioural characteristics.

Identification by who people are!

All the biometrics methods can be divided into:


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INTRODUCTION TO BIOMETRICS

Most popular methods:

voice identification

signature dynamics

keystroke dynamics

motion recognition

BEHAVIOURAL Hand:

hand geometry

hand veins

geometry

fingerprints

palmprints

Head:

eye iris

retina

face recognition

ear

PHYSIOLOGICAL


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GENERAL MOTIVATION FOR EAR BIOMETRICS

WHERE DOWE HEAD ?

passive

physiological

biometrics

FACE AND EAR BIOMETRICS

MIGHT BE THE ANSWER


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FACE BIOMETRICS GENERAL OVERVIEW

Passive physiological method.

Natural humans recognize people by looking at their faces.

Fast development of new algorithms.

Still many unsolved problems including compensation of illuminationchanges and pose invariance.

Some popular methods:

2D geometry,

3D models, PCA, ICA, LDA,

Gabor Wavelets,

HiddenMarkovModels.


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EAR BIOMETRICS

Human ears have been used as major feature in the forensic science

for many years.

Earprints found on the crime scene have been used as a proof in over

few hundreds cases in the Netherlands and the United States.

Human ear contains large amount of specific and unique features thatallows for human identification.

Ear images can be easily taken from a distance and without

knowledge of the examined person. Therefore suitable for security, surveillance, access control and

monitoring applications.


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PASSIVE BIOMETRICS: EAR vs. FACE

Ear does not change during human life, and face changes moresignificantly with age than any other part of human body.

cosmetics, facial hair and hair styling, emotions express differentstates of mind like sadness, happiness, fear or surprise.

Colour distribution is more uniform in ear than in human face, iris orretina.

not much information is lost while working with the greyscale orbinarized images.

Ear is also smaller than face, which means that it is possible to work

faster and more efficiently with the images with the lower resolution. Ear images cannot be disturbed by glasses, beard nor make-up.

However, occlusion by hair or earrings is possible.


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SAMPLE EAR IMAGES FROM OUR DATABASE

Ears differ at a first glance.

We lack in vocabulary - humans just dont look at ears.

easy ear images


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SAMPLE EAR IMAGES FROM OUR DATABASE

Removing hair for access control is simple and

takes just single seconds.

difficult ear images


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EAR BIOMETRICS OBVIOUS APPROACH

How to find

specific points?

The method based on

geometrical distances.


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IANNARELLIS MANUAL MEASUREMENTS

The first, manual method, used by Iannarelli in the research in which

he examined over 10000 ears and proved their uniqueness, was based

on measuring the distances between specific points of the ear.

Iannarelli proved that even twins ears are different.

The major problem in ear identification systems is discovering

automated method to extract those specific, key points.


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EAR BIOMETRICS KNOWN METHODS

Neighborhood graphs based on Voronoi diagrams.Burge M., Burger W., Ear Recognition, in Biometrics: Personal Identification in

Networked Society (eds. Jain A.K., Bolle R., Pankanti S.), 273-286, Kluwer

Academic Publishing, 1998.

BurgeM

., Burger W., Ear Biometrics forM

achine Vision, Proc. Of 21st

Workshop ofthe Austrian Association for Pattern Recognition, Hallstatt, Austria, 1997.

BurgeM., Burger W., Ear Biometrics in Computer Vision, IEEE ICPR 2000.


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Ear Biometrics based on Force Field Transformation

Hurley D.J., NixonM.S., Carter J.N., Automatic Ear Recognition by

Force Field Transformations, IEE Colloquium on Biometrics, 2000.

Hurley D.J., NixonM.S., Carter J.N., Force Field Energy Functionals for

Image Feature Extraction, Image and Vision Computing Journal, vol.

20, no. 5-6, 311-318, 2002.


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Ear Biometrics based on Force Field Transformation

Application of force field transformation in order to find energy lines,

wells and channels as ear features.


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Ear Biometrics based on PCA and eigenearsChang K., Victor B., Bowyer K.W., Sarkar S., Comparison and Combination of Ear and

Face Images for Biometric Recognition, 2003.

Victor B., Bowyer K.W., Sarkar S., An Evaluation of Face and Ear Biometrics, Proc. of

Intl. Conf. on Pattern Recognition, I: 429-432, 2002.Chang K., Victor B., Bowyer K.W., Sarkar S., Comparison and Combination of Ear and

Face Images in Appereance-Based Biometrics, IEEE Trans. on PAMI, vol. 25, no.

9, 2003.

Ear Biometrics based on compression networksMoreno B., Sanchez A., Velez J.F., On the Use of Outer Ear Images for Personal

Identification in Security Applications, IEEE 1999.


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EAR BIOMETRICS OUR APPROACH

Ear Biometrics Based on Geometrical Feature

Extraction

Choras Michal, Feature Extraction Based on Contour Processing in Ear Biometrics,

IEEE Workshop on Multimedia Communications and Services, MCS04, 15-19,

Cracow, 2004.

Choras Michal, Human Ear Identification Based on Image Anlysis, in L. Rutkowski et

al. (Eds): Artificial Intelligence and Soft Computing, ICAISC 2004, Springer-Verlag

LNAI 3070, 688-693, 2004.

ChorasMichal, Ear Biometrics Based on Geometrical Method of Feature Extraction, inF.J Perales and B.A. Draper (Eds.): Articulated Motion and Deformable Objects,

AMDO 2004, Springer-Verlag LNCS 3179, 51-61, 2004.


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GEOMETRICAL FEATURE EXTRACTION

General Overview:

Contour Detection, Normalization

Centroid Calculation

1st Algorithm Based on Concentric Circles 2nd Algorithm Based on Contour Tracing

Feature Vectors Comparison and Classification


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CONCLUSIONS & WORK-IN-PROGRESS

Aim: Developement of the automatic algorithm based on geometricalfeatures for ear identification

So far: Algorithm calculating properties of concentic circlesoriginated in the ear contour image centriod

So far: Algoritm based on contour tracing and extracting of thecharacteristic points

Results: Good for easy ear images.

Remarks: Heavily dependent on contour detection.

Now additional segmentation is used to avoid hair, glasses and

earrings contours.

New algorithm of selecting only 8-10 longest contours is

proposed.


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CONCLUSIONS & WORK-IN-PROGRESS

Work in progress:

Algorithm calculating standard geometrical curve-features

applied to 10 longest ear contours,

New algorithm calculating triangle ratio of the longest

contour,

Classification to left and right ears based on longest contour

direction,

New algorithm calculating modified shape ratios of the 10

longest contours, Further developement of ear database 20 views for a

person (5 orientations, 2 scales, 2 illuminations).

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