Ear Biometrics

Institute of Telecommunication

Image Processing Group

Ear Biometrics for Human Identification Based on Image Analysis

Michal ChorasImage Processing Group


ATR Bydgoszcz, Poland

Presentation for ELCVIA Journal



Michał Choraś-Ear Biometrics for Human Identification

INTRODUCTION TO HUMAN IDENTIFICATION

• Traditional methods: PIN’s 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.




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:




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




GENERAL MOTIVATION FOR EAR BIOMETRICS

• WHERE DO WE HEAD ?

passive

physiological

biometrics

FACE AND EAR BIOMETRICS MIGHT BE THE ANSWER




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 illumination changes and pose invariance.

• Some popular methods: • 2D geometry,

• 3D models,

• PCA, ICA, LDA,

• Gabor Wavelets,

• Hidden Markov Models.




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 that allows 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.




PASSIVE BIOMETRICS: EAR vs. FACE

• Ear does not change during human life, and face changes more significantly with age than any other part of human body.– cosmetics, facial hair and hair styling, emotions express different

states of mind like sadness, happiness, fear or surprise.• Colour distribution is more uniform in ear than in human face, iris or

retina.– not much information is lost while working with the greyscale or

binarized 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.




SAMPLE EAR IMAGES FROM OUR DATABASE

Ears differ „at a first glance”.

We lack in vocabulary - humans just don’t look at ears.

„easy ear images”




SAMPLE EAR IMAGES FROM OUR DATABASE

Removing hair for access control is simple and takes just single seconds.

„difficult ear images”




EAR BIOMETRICS – OBVIOUS APPROACH

How to find specific points?

The method based on geometrical distances.




IANNARELLI’S 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 twin’s ears are different.

• The major problem in ear identification systems is discovering automated method to extract those specific, key points.




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.

Burge M., Burger W., Ear Biometrics for Machine Vision, Proc. Of 21st Workshop of the Austrian Association for Pattern Recognition, Hallstatt, Austria, 1997.

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





• Ear Biometrics based on Force Field TransformationHurley D.J., Nixon M.S., Carter J.N., Automatic Ear Recognition by

Force Field Transformations, IEE Colloquium on Biometrics, 2000.

Hurley D.J., Nixon M.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.





• Ear Biometrics based on Force Field TransformationApplication of force field transformation in order to find energy lines,

wells and channels as ear features.





Ear Biometrics based on PCA and ‘eigenears’Chang 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.




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, MCS’04, 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.

Choras Michal, Ear Biometrics Based on Geometrical Method of Feature Extraction, in F.J Perales and B.A. Draper (Eds.): Articulated Motion and Deformable Objects, AMDO 2004, Springer-Verlag LNCS 3179, 51-61, 2004.




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




CONCLUSIONS & WORK-IN-PROGRESS

• Aim: Developement of the automatic algorithm based on geometrical features for ear identification

• So far: Algorithm calculating properties of concentic circles originated in the ear contour image centriod

• So far: Algoritm based on contour tracing and extracting of the characteristic 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.




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).

Ear Biometrics

Technology

Transcript of Ear Biometrics