Granted Patent B2

US 9,280,701 · App. 14/272,809 · Granted Mar 8, 2016

Abstract

Claims (25)

1. A computer-implemented method for sorting face images of different individuals into different groups, comprising:

obtaining face images comprising faces of unknown individuals by a computer processor;

calculating similarity functions between pairs of face images by the computer processor;

joining face images that have values of the similarity functions above a predetermined threshold into a hypothetical face group, wherein the face images in the hypothetical face group hypothetically belong to a same person;

conducting non-negative matrix factorization on values of the similarity functions in the hypothetical face group to test truthfulness of the hypothetical face group; and

identifying the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is above a threshold based on the non-negative matrix factorization.

2. The computer-implemented method of claim 1 , further comprising:

rejecting the hypothetical face group as a true face group if a percentage of the associated similarity functions being true is below a threshold.

3. The computer-implemented method of claim 1 , wherein the step of conducting non-negative matrix factorization comprises:

forming a non-negative matrix using values of similarity functions between all different pairs of face images in the hypothetical face group,

wherein the non-negative matrix factorization is conducted over the non-negative matrix.

4. The computer-implemented method of claim 1 , wherein the similarity functions in the hypothetical face group are described in a similarity distribution function, wherein the step of non-negative matrix factorization outputs a True similarity distribution function and a False similarity distribution function.

5. The computer-implemented method of claim 4 , wherein the step of identifying comprises:

comparing the similarity distribution function to the True similarity distribution function and the False similarity distribution function.

6. The computer-implemented method of claim 1 , wherein every pair of face images in the hypothetical face group has a similarity function above the predetermined threshold.

7. The computer-implemented method of claim 1 , further comprising:

joining two true face groups to form a joint face group;

conducting non-negative matrix factorization on values of similarity functions in the joint face group; and

merging the two true face groups if a percentage of the associated similarity functions being true is above a threshold in the joint face group.

8. The computer-implemented method of claim 7 , wherein the similarity functions in the joint face group are described in a similarity distribution function, wherein the step of conducting non-negative matrix factorization on values of similarity functions in the joint face group outputs a True similarity distribution function and a False similarity distribution function.

9. The computer-implemented method of claim 8 , wherein the step of identifying comprises:

comparing the similarity distribution function to the True similarity distribution function and the False similarity distribution function.

10. The computer-implemented method of claim 1 , further comprising:

detecting the faces in images; and

cropping portions of the images to produce the face images comprising faces of the unknown individuals.

Assignments (8)