SYSTEMS AND METHODS FOR PERSONA IDENTIFICATION USING COMBINED PROBABILITY MAPS

• US 20160350585A1
• Filed: 08/08/2016
• Published: 12/01/2016
• Est. Priority Date: 12/31/2013
• Status: Active Grant

• Current Assignee: Personify Incorporated (Wilson Human Capital Group, Inc.)
• Original Assignee: Personify Incorporated (Wilson Human Capital Group, Inc.)
• Inventors: Lin, Dennis, Francisco, Glenn, Nguyen, Quang, Dang, Long
• Granted Patent: US 9,740,916 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/251 of input or preprocessed data
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/20076 Probabilistic image processing
• CPC Class Codes: G06T 2207/20228 Disparity calculation for image-based rendering
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/143 involving probabilistic approaches, e.g. Markov random field [MRF] modelling
• CPC Class Codes: G06T 7/149 involving deformable models, e.g. active contour models
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06V 10/755 Deformable models or variational models, e.g. snakes or active contours
• CPC Class Codes: G06V 10/803 of input or preprocessed data
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation
• CPC Class Codes: G06V 40/171 Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: H04N 7/147 Communication arrangements, e.g. identifying the communication as a video-communication, intermediate storage of the signals selecting H04Q
• CPC Class Codes: H04N 7/15 Conference systems

Abstract

Disclosed herein are systems and methods for persona identification using combined probability maps. An embodiment takes the form of a method that includes obtaining at least one frame of pixel data; processing the at least one frame of pixel data to generate a hair-identification probability map; and generating a persona image by extracting pixels from the at least one frame of pixel data based at least in part on the generated hair-identification probability map.

Claims

1. A method comprising:

obtaining at least one frame of pixel data;

processing the at least one frame of pixel data to generate a hair-identification probability map; and

generating a persona image by extracting pixels from the at least one frame of pixel data based at least in part on the generated hair-identification probability map.

2. The method of claim 1, wherein processing the at least one frame of pixel data to generate a hair-identification probability map comprises:

identifying a plurality of pixel columns that cross an identified head contour; and

for each pixel column in the plurality of pixel columns;

performing a color-based segmentation of the pixels in the pixel column into a foreground segment, a hair segment, and a background segment; and

assigning the pixels in the hair segment an increased foreground-probability value in the hair-identification probability map.

3. The method of claim 2, further comprising converting the head contour into a multi-segment polygon that approximates the head contour, the multi-segment polygon being formed of multiple head-contour segments, wherein identifying the plurality of pixel columns that cross the identified head contour comprises identifying pixel columns that cross one of the head-contour segments.

4. The method of claim 2, wherein performing a color-based segmentation comprises performing a color-based segmentation using a clustering algorithm.

5. The method of claim 4, wherein the clustering algorithm is a k-means algorithm with k=3.

6. The method of claim 2, wherein performing the color-based segmentation of the pixels in a given pixel column into the foreground segment, the hair segment, and the background segment of the given pixel column comprises:

identifying an average foreground-pixel color, an average hair-pixel color, and an average background-pixel color for the given pixel column; and

identifying the foreground segment, the hair segment, and the background segment of the given pixel column using a clustering algorithm to cluster the pixels in the given pixel column around the identified average foreground-pixel color, the identified average hair-pixel color, and the identified average background-pixel color for the given pixel column, respectively.

7. The method of claim 6, wherein:

identifying the average foreground-pixel color for the given pixel column comprises identifying the average foreground-pixel color for the given pixel column based on a first set of pixels at an innermost end of the given pixel column;

identifying the average hair-pixel color for the given pixel column comprises identifying the average hair-pixel color for the given pixel column based on a second set of pixels that includes a point where the given pixel column crosses the identified head contour; and

identifying the average background-pixel color for the given pixel column comprises identifying the average background-pixel color for the given pixel column based on a third set of pixels at an outermost end of the given pixel column.

8. The method of claim 2, further comprising, for each pixel column in the plurality of pixel columns:

assigning the pixels in the foreground and background segments an equal probability of being in the foreground and being in the background in the hair-identification-module persona probability map.

9. The method of claim 2, wherein assigning the pixels in the hair segment an increased foreground-probability value in the hair-identification-module persona probability map comprises:

assigning a first value to the pixels in the hair segment in the hair-identification-module persona probability map; and

assigning a second value to the pixels in the foreground and background segments in the hair-identification-module persona probability map, wherein the first value corresponds to a higher probability of being a foreground pixel than does the second value.

10. The method of claim 2, further comprising processing the at least one frame of pixel data to generate at least one additional probability map, wherein generating the persona image by extracting pixels from the at least one frame of pixel data is further based on the generated at least one additional probability map.

11. The method of claim 10, wherein:

obtaining the at least one frame of pixel data comprises obtaining the at least one frame of pixel data and corresponding image depth data; and

processing the at least one frame of pixel data to generate the at least one additional probability map comprises processing the at least one frame of pixel data and the corresponding image depth data to generate at least one of the at least one additional probability map.

12. The method of claim 10, further comprising combining the hair-identification probability map and the at least one additional probability map to obtain an aggregate persona probability map, wherein generating the persona image by extracting pixels from the at least one frame of pixel data based at least in part on the generated hair-identification probability map and at least in part on the generated at least one additional probability map comprises generating the persona image by extracting pixels from the at least one frame of pixel data based on the aggregate persona probability map.

13. The method of claim 1, further comprising processing the at least one frame of pixel data to generate at least one additional probability map, wherein generating the persona image by extracting pixels from the at least one frame of pixel data is further based at least in part on the generated at least one additional probability map.

14. The method of claim 13, wherein:

obtaining the at least one frame of pixel data comprises obtaining the at least one frame of pixel data and corresponding image depth data; and

processing the at least one frame of pixel data to generate the at least one additional probability map comprises processing the at least one frame of pixel data and the corresponding image depth data to generate at least one of the at least one additional probability map.

15. The method of claim 13, further comprising combining the hair-identification probability map and the at least one additional probability map to obtain an aggregate persona probability map, wherein generating the persona image by extracting pixels from the at least one frame of pixel data based at least in part on the generated hair-identification probability map and at least in part on the generated at least one additional probability map comprises generating the persona image by extracting pixels from the at least one frame of pixel data based on the aggregate persona probability map.

16. An apparatus comprising:

a hair-identification module that is configured to generate a hair-identification probability map based on at least one frame of pixel data at least in part by;

identifying a plurality of pixel columns that cross an identified head contour; and

for each pixel column in the plurality of pixel columns;

performing a color-based segmentation of the pixels in the pixel column into a foreground segment, a hair segment, and a background segment; and

assigning the pixels in the hair segment an increased foreground-probability value in the hair-identification probability map; and

a persona extraction module configured to generate a persona image by extracting pixels from at least one frame of pixel data based at least in part on the generated hair-identification probability map.

17. The apparatus of claim 16, wherein the hair-identification module is configured to generate the hair-identification probability map based on the at least one frame of pixel data at least in part by:

identifying a plurality of pixel columns that cross an identified head contour; and

for each pixel column in the plurality of pixel columns;

performing a color-based segmentation of the pixels in the pixel column into a foreground segment, a hair segment, and a background segment; and

assigning the pixels in the hair segment an increased foreground-probability value in the hair-identification probability map.

18. The apparatus of claim 17, further comprising a foreground-background module configured to generate a foreground-background map based on image depth data corresponding to the at least one frame of pixel data, wherein the persona extraction module is configured to generate the persona image by extracting pixels from the at least one frame of pixel data based also on the generated foreground-background map.

19. The apparatus of claim 17, further comprising:

a plurality of additional persona identification modules configured to generate a corresponding plurality of additional persona probability maps based on the at least one frame of pixel data; and

a combiner module configured to generate an aggregate persona probability map based on the hair-identification probability map and the plurality of additional persona probability maps,

wherein the persona extraction module being configured to generate the persona image by extracting pixels from the at least one frame of pixel data based at least in part on the generated hair-identification probability map comprises the persona extraction module being configured to generate the persona image by extracting pixels from the at least one frame of pixel data based on the aggregate persona probability map.

20. The apparatus of claim 19, further comprising:

a foreground-background module configured to generate a foreground-background map based on image depth data corresponding to the at least one frame of pixel data,

wherein one or more of the additional persona identification modules are configured to generate their respective corresponding additional persona probability map based further on the generated foreground-background probability map.