PERSON CENTRIC TRAIT SPECIFIC PHOTO MATCH RANKING ENGINE

• US 20180075317A1
• Filed: 09/09/2016
• Published: 03/15/2018
• Est. Priority Date: 09/09/2016
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: GOMEZ SUAREZ, Federico E., CANTON FERRER, Cristian
• Application Number:
  US15/260,506
• Publication Number:
  US 20180075317A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 18/217 Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06T 1/0007 Image acquisition
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation

Abstract

In a face recognition system, a face classifier is configured to receive an input image, and analyze the input image to determine at least one specific trait. A feature extractor is configured to receive a plurality of data sets based on the determined specific trait, and generate a plurality of feature sets corresponding to the plurality of data sets, wherein respective ones of the feature sets include corresponding features extracted from respective ones of the data sets. A feature comparator is configured to receive a plurality of images from an image database, compare the input image against the plurality of images from the image database by using the plurality of feature sets generated by the feature extractor, and output a ranking of potential matches indicating a likelihood of a match between the input image and the plurality of images in the image database.

Claims

1. A face recognition system comprising:

a face classifier configured to

receive an input image, and

analyze the input image to determine at least one specific trait;

a feature extractor configured to

receive a plurality of data sets based on the determined specific trait, and

generate a plurality of feature sets corresponding to the plurality of data sets, wherein respective ones of the feature sets include corresponding features extracted from respective ones of the data sets; and

a feature comparator configured to

receive a plurality of images from an image database,

compare the input image against the plurality of images from the image database by using the plurality of feature sets generated by the feature extractor, and

selects potential matches between the input image and the plurality of images in the image database.

2. The face recognition system of claim 1, wherein respective ones of the data sets include pairs of images with each pair including one image that includes the specific trait and another image that does not include the specific trait.

3. The face recognition system of claim 1, wherein the specific trait is one or more of age range, race, skin color, gender, a scar, or a tattoo.

4. The face recognition system of claim 1, wherein the feature extractor is configured to generate a plurality of vectors that includes a plurality of elements indicating respective features extracted from respective data sets.

5. The face recognition system of claim 1, wherein the face classifier is configured to compare the received image against a database of images to determine the one or more specific traits.

6. The face recognition system of claim 1, wherein the feature comparator a ranking of potential matches indicating a likelihood of a match between the input image and the plurality of images in the image database.

7. The face recognition system of claim 1, wherein the feature extractor comprises a convolutional neural network (CNN) configured to generate the plurality of feature sets.

8. The face recognition system of claim 1, where the feature comparator is configured to generate a recognition decision for the input image, wherein the recognition decision is one of (i) subject verification decision or (ii) subject recognition decision.

9. A tangible, non-transitory computer readable medium, or media, storing machine readable instructions that, when executed by one or more processors, cause the one or more processors to:

receive an input image;

analyze the input image to determine at least one specific trait;

receive a plurality of data sets based on the determined specific trait;

generate a plurality of feature sets corresponding to the plurality of data sets, wherein respective ones of the feature sets include corresponding features extracted from respective ones of the data sets;

receive a plurality of images from an image database;

compare the input image against the plurality of images from the image database by using the plurality of feature sets; and

select potential matches between the input image and the plurality of images in the image database.

11. The non-transitory computer-readable medium or media of claim 9, wherein the machine readable instructions, when executed by the one or more processors, cause the one or more processors to:

generate a recognition decision for the input image, wherein the recognition decision is one of (i) subject verification decision or (ii) subject recognition decision.

12. The non-transitory computer-readable medium or media of claim 9, wherein respective ones of the data sets include pairs of images with each pair including one image that includes the specific trait and another image that does not include the specific trait.

13. The non-transitory computer-readable medium or media of claim 9, wherein the specific trait is one or more of age range, race, skin color, gender, a scar, or a tattoo.

14. The non-transitory computer-readable medium or media of claim 9, wherein the machine readable instructions, when executed by one or more processors, cause the one or more processors to apply a convolutional neural network (CNN) configured to generate the plurality of feature sets.

15. The non-transitory computer-readable medium or media of claim 9, wherein generating a plurality of feature sets corresponding to the plurality of data sets comprises generating a plurality of vectors that includes a plurality of elements indicating respective features extracted from respective data sets.

10. The tangible, non-transitory computer readable medium, or media, storing machine readable instructions that, when executed by one or more processors, according to claim 10, cause the one or more processors to:

output a ranking of potential matches indicating a likelihood of a match between the input image and the plurality of images in the image database.

16. A method for recognizing faces in a face recognition system, the method comprising:

receiving an input image;

analyzing the input image to determine at least one specific trait;

receiving a plurality of data sets based on the determined specific trait;

generating a plurality of feature sets corresponding to the plurality of data sets, wherein respective ones of the feature sets include corresponding features extracted from respective ones of the data sets;

receiving a plurality of images from an image database;

comparing the input image against the plurality of images from the image database by using the plurality of feature sets; and

outputting a ranking of potential matches indicating a likelihood of a match between the input image and the plurality of images in the image database.

17. The method of claim 16, wherein respective ones of the data sets include pairs of images with each pair including one image that includes the specific trait and another image that does not include the specific trait.

18. The method of claim 16, wherein the specific trait is one or more of age range, race, skin color, gender, a scar, or a tattoo.

19. The method of claim 16, further comprising:

generating a recognition decision for the input image, wherein the recognition decision is one of (i) subject verification decision or (ii) subject recognition decision.

20. The method of claim 16, wherein generating a plurality of feature sets comprises a convolutional neural network (CNN) configured to generate the plurality of feature sets.