Your SlideShare is downloading. ×
Ear Biometrics
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Ear Biometrics

6,552

Published on

Published in: Technology, Health & Medicine
1 Comment
3 Likes
Statistics
Notes
No Downloads
Views
Total Views
6,552
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
298
Comments
1
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Ear Biometrics for Human Identification Based on Image Analysis Micha l Chora s Image Processing Group Institute of Telecommunication ATR Bydgoszcz, Poland Presentation for ELCVIA Journal
  • 2. 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
      • invasiveness
      • Identification by something that people know or possess.
  • 3. INTRODUCTION TO BIOMETRICS
    • Definition : automatic identification of a living person based on physiological or behavio u ral characteristics .
    • Identification by who people are!
    • All the biometrics methods can be divided into:
  • 4. 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
  • 5. GENERAL MOTIVATION FOR EAR BIOMETRICS
    • WHERE DO WE HEAD ?
    passive physiological biometrics FACE AND EAR BIOMETRICS MIGHT BE THE ANSWER
  • 6. 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 m odels,
        • PCA , ICA , LDA ,
        • Gabor Wavelets,
        • Hidden Markov Models.
  • 7. 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.
  • 8. 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.
  • 9. SAMPLE EAR IMAGES FROM OUR DATABASE Ear s differ „at a first glance”. We lack in vocabulary - humans just don’t look at ears . „ easy ear images”
  • 10. SAMPLE EAR IMAGES FROM OUR DATABASE Removing hair for access control is simple and takes just single seconds. „ difficult ear images”
  • 11. EAR BIOMETRICS – OBVIOUS APPROACH How to find specific points? The method based on geometrical distances.
  • 12. 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.
  • 13. 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 21 st Workshop of the Austrian Association for Pattern Recognition, Hallstatt, Austria, 1997.
    • Burge M., Burger W., Ear Biometrics in Computer Vision, IEEE ICPR 2000.
  • 14. EAR BIOMETRICS – KNOWN METHODS
    • Ear Biometrics based on Force Field Transformation
    • Hurley 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.
  • 15. EAR BIOMETRICS – KNOWN METHODS
    • Ear Biometrics based on Force Field Transformation
    • Application of force field transformation in order to find energy lines, wells and channels as ear features.
  • 16. EAR BIOMETRICS – KNOWN METHODS
    • 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 networks
    • Moreno B., Sanchez A., Velez J.F., On the Use of Outer Ear Images for Personal Identification in Security Applications, IEEE 1999.
  • 17. EAR BIOMETRICS – OUR APPROACH
    • Ear Biometrics Based on Geometrical Feature Extraction
    • Chora s Micha l , Feature Extraction Based on Contour Processing in Ear Biometrics, IEEE Workshop on Multimedia Communications and Services, MCS’04, 15-19, Cracow, 2004 .
    • Chora s Micha l , Human Ear Identification Based on Image Anlysis, in L. Rutkowski et al. (Eds): Artificial Intel l igence and Soft Computing, ICAISC 2004, Springer-Verlag LNAI 3070, 688-693, 2004.
    • Chora s Micha l , 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.
  • 18. 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
  • 19. 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.
  • 20. 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 – 2 0 views for a person ( 5 orientations, 2 scales, 2 illuminations).

×