Efficient classification of three dimensional face models for human identification and other applications

• US 20050226509A1
• Filed: 03/30/2005
• Published: 10/13/2005
• Est. Priority Date: 03/30/2004
• Status: Abandoned Application

• Current Assignee: Fish & Richardson PC
• Original Assignee: Fish & Richardson PC
• Inventors: Tsaregorodtsev, Alexei, Medioni, Gerard, Maurer, Thomas, Waupotitsch, Roman, Maslov, Igor
• Application Number:
  US11/096,285
• Publication Number:
  US 20050226509A1
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06V 20/653 by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

Three dimensional face recognition via vectorizing samples that are in an enrollment data base. The vectors are formed by comparing faces in the enrollment database with reference faces, and determining differences between the actual faces and the reference faces. Those differences are then formed into an N dimensional vector representing the classified faces. A query face is then similarly vectorized and compared to precomputed vectors indicative of the faces in the database. Another technique is described for updating the reference faces based on an error level.

Claims

1. A method comprising

obtaining a plurality of reference faces;

obtaining a plurality of enrollment faces, representing faces about which information is already known;

comparing at least a plurality of said enrollment faces with said reference faces to produce a plurality of enrollment scores representing differences between each of the plurality of enrollment faces and said reference faces;

obtaining a query face, and comparing said query face with said reference faces to produce a plurality of query scores representing differences between said query face and said reference faces; and

comparing said query scores with said plurality of enrollment scores to determine matches between said query face and said plurality of enrollment faces.

2. A method as in claim 1, wherein said reference faces, said enrollment faces, and said query faces represent three dimensional face shapes.

3. A method as in claim 2, further comprising further processing said matches, by comparing a three dimensional face shapes between said query face, and said matches.

4. A method as in claim 2, wherein said plurality of query scores form a multiple dimensional vector, and said comparing scores comprises comparing vectors.

5. A method as in claim 3, wherein said further processing comprises comparing complete face shapes for said matches, to determine if more than one model in the database represents said face shape.

6. A method as in claim 2, wherein at least a plurality of said reference faces represents only a portion of a face shape.

7. A method as in claim 2, further comprising updating the set of reference faces.

8. A method as in claim 7, wherein said updating comprises determining a new set of reference faces, recomputing said scores, and using said new scores for said recognizing.

9. A method as in claim 7, further comprising monitoring an error in said matching, determining said error being higher than a specified amount, and updating the set of reference faces when said error becomes higher than the specified amount.

10. A method as in claim 1, further comprising comparing an aspect of said query face to said enrollment faces using a two-dimensional face matching technique.

11. A method comprising:

converting a plurality of three-dimensional enrollment models into N-dimensional enrollment model vectors, by comparing the plurality of enrollment models to reference models, obtaining differences between each of the plurality of enrollment models and each of the reference models, and using said differences to form said N dimensional enrollment model vectors;

converting a query model, representing a face to be recognized, into an n dimensional query model vector, by comparing the query model to said reference models, obtaining the differences between the query model and the reference models, and using said differences to form an N dimensional query vector;

comparing the query vector to said enrollment model vectors and producing information indicative of matches therebetween.

12. A method as in claim 11 further comprising comparing said at least one match to said three-dimensional models, by comparing the entire query model to the entire three-dimensional model representing said at least one match.

13. A method as in claim 11, further comprising determining multiple best matches between the query model and three-dimensional models representing said at least one match.

14. A method as in claim 11, further comprising monitoring for errors in said comparing, and updating said reference models based on said errors having a certain level.

15. A method as in claim 11, wherein said reference models comprise partial face masks, and said converting comprises comparing the models to the partial face mask.

16. A method as in claim 11 further comprising comparing an aspect of said query face to said enrollment faces using a two-dimensional face matching technique.

17. A method as in claim 14, wherein said monitoring for errors comprises monitoring a performance of said n dimensional vectors as the contents of the database are changed.

18. A method as in claim 11, wherein said comparing comprises determining if the query is within a specified threshold of one of the n dimensional vectors.

19. A method as in claim 18, wherein said specified threshold is different for different faces.

20. A method as in claim 18, further comprising using an individual threshold for each of a plurality of different faces.

21. A method as in claim 20, further comprising comparing a face to other faces, determining a score for said comparing, and using said score to determine said individual threshold.

22. A method as in claim 21, wherein said using said score comprises selecting a smallest score as the threshold.

23. A system comprising

a database, storing information about a plurality of enrollment faces, representing faces about which information is already known, and storing a plurality of enrollment vectors, representing differences between each of the plurality of enrollment faces and a plurality of reference faces;

a query station, that obtains a query face, compares said query face with said reference faces to produce a query vector representing differences between said query face and said reference faces, and compares said query vector with said plurality of enrollment vectors to determine matches between said query face and said plurality of enrollment faces, and produces information indicative of said matches.

24. A system as in claim 23, wherein said reference faces, said enrollment faces, and said query faces represent three dimensional face shapes.

25. A system as in claim 24, wherein said query station also further processes said matches, by comparing complete three dimensional face shapes between said query face, and said matches.

26. A system as in claim 24, wherein at least a plurality of said reference faces represents only a portion of the shape of the reference face.

27. A system as in claim 24, wherein said query station further operates to update the set of reference faces.