IMAGE ENHANCEMENT AND FEATURE EXTRACTION FOR OCULAR-VASCULAR AND FACIAL RECOGNITION

• US 20170076145A1
• Filed: 09/09/2016
• Published: 03/16/2017
• Est. Priority Date: 09/11/2015
• Status: Active Grant

• Current Assignee: Jumio Inc.
• Original Assignee: EyeVerify, Inc. (Ant Group Co., Ltd.)
• Inventors: Gottemukkula, Vikas, Derakhshani, Reza R., Saripalle, Sashi K.
• Granted Patent: US 9,721,150 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/56 relating to colour
• CPC Class Codes: G06V 10/751 Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
• CPC Class Codes: G06V 10/993 Evaluation of the quality of the acquired pattern
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation
• CPC Class Codes: G06V 40/172 Classification, e.g. identification
• CPC Class Codes: G06V 40/193 Preprocessing; Feature extraction
• CPC Class Codes: G06V 40/197 Matching; Classification

Abstract

Biometric enrollment and verification techniques for ocular-vascular, periocular, and facial regions are described. Periocular image regions can be defined based on the dimensions of an ocular region identified in an image of a facial region. Feature descriptors can be generated for interest points in the ocular and periocular regions using a combination of patterned histogram feature descriptors. Quality metrics for the regions can be determined based on region value scores calculated based on texture surrounding the interest points. A biometric matching process for calculating a match score based on the ocular and periocular regions can progressively include additional periocular regions to obtain a greater match confidence.

Claims

1. A computer-implemented method comprising:

receiving an image of a facial region of a user, the facial region including an eye and an area surrounding the eye;

processing the image to;

(i) define an ocular image region including at least a portion of the eye in the image of the facial region and (ii) define one or more periocular image regions each including at least a portion of the area surrounding the eye in the image of the facial region;

identifying a plurality of points of interest in at least one of the ocular image region and the one or more periocular image regions;

generating, for each point of interest, a feature descriptor based on a combination of a plurality of patterned histogram feature descriptors; and

storing the generated feature descriptors in a biometric template.

2. The method of claim 1, wherein processing the image comprises enhancing at least a portion of the image using local gradient Gabor pattern (LGGP).

3. The method of claim 2, wherein enhancing at least a portion of the image using LGGP comprises:

calculating, at each of a plurality of angles, a Gabor phase image for the at least a portion of the image;

aggregating the calculated Gabor phase images to form a combined Gabor phase image;

calculating, at each of a plurality of angles, a local gradient of the combined Gabor phase image; and

retaining a maximum value of each local gradient to form an enhanced image.

4. The method of claim 1, wherein a particular patterned histogram feature descriptor comprises a patterned histogram of extended multi-radii local binary patterns (PH-EMR-LBP), a patterned histogram of extended multi-radii center symmetric local binary patterns (PH-EMR-CSLBP), or a patterned histogram of extended multi-radii local ternary patterns (PH-EMR-LTP).

5. The method of claim 4, wherein generating the feature descriptor for a particular point of interest comprises generating a PH-EMR-LBP feature descriptor by:

defining an image region comprising the particular point of interest;

calculating a plurality of local binary pattern (LBP) codes for each pixel in the image region to form a multi-radii LBP (MR-LBP) image;

dividing the MR-LBP image into a plurality of sub-regions;

deriving a plurality of histograms comprising frequencies of each MR-LBP bit location within each sub-region; and

combining the plurality of histograms to form the PH-EMR-LBP feature descriptor.

6. The method of claim 4, wherein generating the feature descriptor for a particular point of interest comprises generating a PH-EMR-CSLBP feature descriptor by:

defining an image region comprising the particular point of interest;

calculating a plurality of center symmetric local binary pattern (CSLBP) codes for each pixel in the image region to form a multi-radii CSLBP (MR-CSLBP) image;

dividing the MR-LBP image into a plurality of sub-regions;

deriving a plurality of histograms comprising frequencies of each MR-CSLBP bit location within each sub-region; and

combining the plurality of histograms to form the PH-EMR-CSLBP feature descriptor.

7. The method of claim 4, wherein generating the feature descriptor for a particular point of interest comprises generating a PH-EMR-LTP feature descriptor by:

defining an image region comprising the particular point of interest;

calculating a plurality of local ternary pattern (LTP) codes for each pixel in the image region to form a multi-radii LTP (MR-LTP) image;

dividing the MR-LBP image into a plurality of sub-regions;

deriving a plurality of histograms comprising frequencies of each MR-LTP bit location within each sub-region; and

combining the plurality of histograms to form the PH-EMR-LTP feature descriptor.

8. The method of claim 4, wherein generating the feature descriptor for a particular point of interest comprises:

calculating a PH-EMR-LBP feature descriptor, PH-EMR-CS-LBP feature descriptor, and PH-EMR-LTP feature descriptor for the particular point of interest; and

combining the PH-EMR-LBP feature descriptor, PH-EMR-CS-LBP feature descriptor, and PH-EMR-LTP feature descriptor to form a combined feature descriptor.

9. The method of claim 8, wherein generating the feature descriptor for a particular point of interest further comprises applying a variance analysis to the combined feature descriptor to form a top feature descriptor comprising a subset of features from the combined feature descriptor.

10. The method of claim 9, wherein generating the feature descriptor for a particular point of interest further comprises performing dimensionality reduction on the top feature descriptor to form the feature descriptor for the particular point of interest.

11. The method of claim 1, wherein, in generating a particular patterned histogram feature descriptor, local binary pattern or local ternary pattern feature descriptors are calculated using a neighborhood defined by a square with or without corners.

12. A system comprising:

at least one memory for storing computer-executable instructions; and

at least one processing unit for executing the instructions stored on the at least one memory, wherein execution of the instructions programs the at least one processing unit to perform operations comprising;

receiving an image of a facial region of a user, the facial region including an eye and an area surrounding the eye;

processing the image to;

(i) define an ocular image region including at least a portion of the eye in the image of the facial region and (ii) define one or more periocular image regions each including at least a portion of the area surrounding the eye in the image of the facial region;

identifying a plurality of points of interest in at least one of the ocular image region and the one or more periocular image regions;

generating, for each point of interest, a feature descriptor based on a combination of a plurality of patterned histogram feature descriptors; and

storing the generated feature descriptors in a biometric template.

13. The system of claim 12, wherein processing the image comprises enhancing at least a portion of the image using local gradient Gabor pattern (LGGP).

14. The system of claim 13, wherein enhancing at least a portion of the image using LGGP comprises:

calculating, at each of a plurality of angles, a Gabor phase image for the at least a portion of the image;

aggregating the calculated Gabor phase images to form a combined Gabor phase image;

calculating, at each of a plurality of angles, a local gradient of the combined Gabor phase image; and

retaining a maximum value of each local gradient to form an enhanced image.

15. The system of claim 12, wherein a particular patterned histogram feature descriptor comprises a patterned histogram of extended multi-radii local binary patterns (PH-EMR-LBP), a patterned histogram of extended multi-radii center symmetric local binary patterns (PH-EMR-CSLBP), or a patterned histogram of extended multi-radii local ternary patterns (PH-EMR-LTP).

16. The system of claim 15, wherein generating the feature descriptor for a particular point of interest comprises generating a PH-EMR-LBP feature descriptor by:

defining an image region comprising the particular point of interest;

calculating a plurality of local binary pattern (LBP) codes for each pixel in the image region to form a multi-radii LBP (MR-LBP) image;

dividing the MR-LBP image into a plurality of sub-regions;

deriving a plurality of histograms comprising frequencies of each MR-LBP bit location within each sub-region; and

combining the plurality of histograms to form the PH-EMR-LBP feature descriptor.

17. The system of claim 15, wherein generating the feature descriptor for a particular point of interest comprises generating a PH-EMR-CSLBP feature descriptor by:

defining an image region comprising the particular point of interest;

calculating a plurality of center symmetric local binary pattern (CSLBP) codes for each pixel in the image region to form a multi-radii CSLBP (MR-CSLBP) image;

dividing the MR-LBP image into a plurality of sub-regions;

deriving a plurality of histograms comprising frequencies of each MR-CSLBP bit location within each sub-region; and

combining the plurality of histograms to form the PH-EMR-CSLBP feature descriptor.

18. The system of claim 15, wherein generating the feature descriptor for a particular point of interest comprises generating a PH-EMR-LTP feature descriptor by:

defining an image region comprising the particular point of interest;

calculating a plurality of local ternary pattern (LTP) codes for each pixel in the image region to form a multi-radii LTP (MR-LTP) image;

dividing the MR-LBP image into a plurality of sub-regions;

deriving a plurality of histograms comprising frequencies of each MR-LTP bit location within each sub-region; and

combining the plurality of histograms to form the PH-EMR-LTP feature descriptor.

19. The system of claim 15, wherein generating the feature descriptor for a particular point of interest comprises:

calculating a PH-EMR-LBP feature descriptor, PH-EMR-CS-LBP feature descriptor, and PH-EMR-LTP feature descriptor for the particular point of interest; and

combining the PH-EMR-LBP feature descriptor, PH-EMR-CS-LBP feature descriptor, and PH-EMR-LTP feature descriptor to form a combined feature descriptor.

20. The system of claim 19, wherein generating the feature descriptor for a particular point of interest further comprises applying a variance analysis to the combined feature descriptor to form a top feature descriptor comprising a subset of features from the combined feature descriptor.

21. The system of claim 20, wherein generating the feature descriptor for a particular point of interest further comprises performing dimensionality reduction on the top feature descriptor to form the feature descriptor for the particular point of interest.

22. The system of claim 12, wherein, in generating a particular patterned histogram feature descriptor, local binary pattern or local ternary pattern feature descriptors are calculated using a neighborhood defined by a square with or without corners.