Feature Extraction Algorithm for Automatic Ear Recognition

• US 20080013794A1
• Filed: 09/06/2005
• Published: 01/17/2008
• Est. Priority Date: 09/08/2004
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Kalker, Antonius Adrianus, Akkermans, Antonius
• Application Number:
  US11/574,759
• Publication Number:
  US 20080013794A1
• Time in Patent Office: Days
• US Class Current: 382/115
• CPC Class Codes: G06V 10/431 Frequency domain transformation; Autocorrelation
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: G07C 9/37 using biometric data, e.g. fingerprints, iris scans or voice recognition

Abstract

The present invention relates to a method and a system of recognizing an ear by locating an invariant point in a representation X of ear geometry. An idea of the present invention is the improve the well known Iannarelli algorithm in that the scheme of the present invention captures and processes all pixels values along an axis and may use an arbitrary number of axes to combine these pixel values to a complete feature vector with a sufficient level of discrimination. The prior art Iannarelli method is improved by performing a Fourier transformation of a polar representation e[θ, p] of the ear, whereby a transformed E[Θ/P] polar representation is created. This transformed representation is sampled to create an ear feature vector X_F.

Claims

1. A method of recognizing an ear by locating an invariant point in a representation (X) of ear geometry, the method comprising the steps of:

creating a polar representation (e[θ

, ρ

]) of the ear geometry;

transforming the polar representation by means of a Fourier transformation, wherein a transformed (E[Θ

, P]) polar representation is created; and

sampling the transformed polar representation using a number of samples (n) to create a feature vector (X_F) comprising a number (m) of feature components.

2. The method according to claim 1, wherein the Fourier transform is a Fourier-Mellin Transform.

3. The method according to claim 1, wherein said invariant point in a representation (X) of the ear geometry is the center of the ear.

4. The method according to claim 1, further comprising the step of determining a distance (d) between a first (X_F) and a second (Y_F) feature vector, wherein correspondence exists between the first and the second feature vector if said distance complies with a predetermined distance value.

5. The method according to claim 4, wherein the determined distance (d) is compared to a predetermined threshold value (T), wherein the first feature vector (X_F) is considered to match the second feature vector (Y_F) if the value of said determined distance is less than said threshold value.

6. The method according to claim 4, wherein the determined distance between the first (X_F) and the second feature vector (Y_F) is the Euclidian distance.

7. The method according to claim 1, wherein the step of locating an invariant point in a representation (X) of ear geometry comprises the step of correlating the representation of ear geometry with a predetermined representation of a typical ear.

8. The method according to claim 7, wherein the step of locating an invariant point in a representation (X) of ear geometry comprises the step of correlating the representation of ear geometry with a center part of the predetermined representation of a typical ear.

17. A computer program product comprising executable components for causing a device having computing capabilities to perform the steps recited in claim 8 when the components are executed in said device having computing capabilities.

9. A system for recognizing an ear by locating an invariant point in a representation (X) of ear geometry, the system comprising means (301) for creating a polar representation (e[θ

, ρ

]) of the ear geometry, transforming the polar representation by means of a Fourier transformation, wherein a transformed (E[Θ

, P]) polar representation is created, and sampling the transformed polar representation using a number of samples (n) to create a feature vector (X_F) comprising a number (m) of feature components.

10. The system according to claim 9, wherein the Fourier transform is a Fourier-Mellin Transform.

11. The system according to claim 9, wherein said invariant point in a representation (X) of the ear geometry is the center of the ear.

12. The system according to claim 9, further comprising means (301, 306) for determining a distance (d) between a first (X_F) and a second (Y_F) feature vector, wherein correspondence exists between the first and the second feature vector if said distance complies with a predetermined distance value.

13. The system according to claim 12, wherein the determining means (301, 306) is further arranged to compare the distance (d) to a predetermined threshold value (T), wherein the first feature vector (X_F) is considered to match the second feature vector (Y_F) if the value of said determined distance is less than said threshold value.

14. The system according to claim 12, wherein the determined distance between the first (X_F) and the second feature vector (Y_F) is the Euclidian distance.

15. The system according to claim 9, wherein the means (301) for creating a polar representation (e[θ

, ρ

]) of the ear geometry is further arranged to locate an invariant point in the representation (X) of ear geometry by correlating said representation of ear geometry with a predetermined representation of a typical ear.

16. The system according to claim 15, wherein the means (301) for creating a polar representation (e[θ

, ρ

]) of the ear geometry is further arranged to correlate the representation (X) of ear geometry with a center part of the predetermined representation of a typical ear.