Face searching and detection in a digital image acquisition device

• US 8,923,564 B2
• Filed: 02/10/2014
• Issued: 12/30/2014
• Est. Priority Date: 03/05/2007
• Status: Active Grant

• Current Assignee: FotoNation Limited (Adeia, Inc.)
• Original Assignee: DigitalOptics Corporation Europe Limited (Adeia, Inc.)
• Inventors: Steinberg, Eran, Bigioi, Petronel, Corcoran, Peter, Gangea, Mihnea, Petrescu, Stefan Mirel, Vasiliu, Andrei, Costache, Gabriel, Drimbarean, Alexandru
• Primary Examiner(s): Li, Ruiping
• Application Number:
  US14/177,212
• Publication Number:
  US 20140153780A1
• Time in Patent Office: 323 Days
• Field of Search: 382/103, 382/190, 382/228, 382/118
• US Class Current: 382/103
• CPC Class Codes: G06V 40/16 Human faces, e.g. facial parts, sketches or expressions
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation
• CPC Class Codes: G06V 40/162 using pixel segmentation or colour matching
• CPC Class Codes: G06V 40/164 using holistic features
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation
• CPC Class Codes: G06V 40/172 Classification, e.g. identification

Abstract

A method of detecting a face in an image includes performing face detection within a first window of the image at a first location. A confidence level is obtained from the face detection indicating a probability of the image including a face at or in the vicinity of the first location. Face detection is then performed within a second window at a second location, wherein the second location is determined based on the confidence level.

Claims

1. A method comprising:

acquiring data of a digital image which depicts one or more objects;

determining a first size of a first detection window and an initial location of the first detection window in the digital image;

wherein the first detection window encompasses a first portion of the digital image;

determining, for the first detection window, first detection window data that represents the first detection window at a first image resolution level;

determining a first set of face detection tests that are to be applied to the first detection window data;

applying the first set of face detection tests to the first detection window data to determine a first confidence level value that indicates a likelihood that a particular object, of the one or more objects, is included in the first detection window;

in response to determining that the first confidence level value is not greater than a first threshold value;

determining a second size that is greater than the first size;

determining a second detection window that has the second size;

wherein the second detection window encompasses a second portion of the digital image;

determining, for the second detection window, second detection window data that represents the second detection window at a second image resolution level;

determining a second set of face detection tests that are to be applied to the second detection window data; and

applying the second set of face detection tests to the second detection window data to determine a second confidence level value that indicates a likelihood that the particular object, of the one or more objects, is included in the second detection window; and

wherein the method is performed using one or more computing devices.

2. The method of claim 1, further comprising:

in response to determining that the second confidence level value is greater than a second threshold value, determining that the particular object, of the one or more objects, is depicted in the second detection window.

3. The method of claim 2, further comprising:

in response to determining that the second confidence level value is not greater than the second threshold value;

determining a second location by rotating the second detection window;

determining a third detection window, wherein the third detection window has the second size and is located at the second location;

determining, for the third detection window, third detection window data;

determining a third set of face detection tests that are to be applied to the third detection window data; and

applying the third set of face detection tests to the third detection window data to determine a third confidence level value that indicates a likelihood that the particular object, of the one or more objects, is included in the third detection window.

4. The method of claim 3, further comprising:

in response to determining that the third confidence level value is greater than a third threshold value, determining that the particular object, of the one or more objects, is depicted in the third detection window.

5. The method of claim 4, further comprising:

in response to determining that the first confidence level value is greater than the first threshold value;

determining a fourth set of face detection tests that are to be applied to the first detection window data;

wherein the fourth set of face detection tests comprises a chain of classifiers to be applied to the first detection window data to determine a skin map; and

applying the fourth set of face detection tests to the first detection window data to determine a fourth confidence level value that indicates a likelihood that the particular object, of the one or more objects, included in the first detection window, is a face.

6. The method of claim 5, wherein the fourth set of face detection tests comprises one or more frontal face detection tests.

7. The method of claim 6,

wherein the skin map comprises a map of values computed for each pixel of the first detection window;

and

wherein a particular value in the skin map represents a probability that a particular pixel of the first detection window depicts a portion of a human skin.

8. A non-transitory computer-readable storage medium, storing one or more instructions which, when executed by one or more processors, cause the one or more processors to perform:

acquiring data of a digital image which depicts one or more objects;

determining a first size of a first detection window and an initial location of the first detection window in the digital image;

wherein the first detection window encompasses a first portion of the digital image;

determining, for the first detection window, first detection window data that represents the first detection window at a first image resolution level;

determining a first set of face detection tests that are to be applied to the first detection window data;

applying the first set of face detection tests to the first detection window data to determine a first confidence level value that indicates a likelihood that a particular object, of the one or more objects, is included in the first detection window;

in response to determining that the first confidence level value is not greater than a first threshold value;

determining a second size that is greater than the first size;

determining a second detection window that has the second size;

wherein the second detection window encompasses a second portion of the digital image;

determining, for the second detection window, second detection window data that represents the second detection window at a second image resolution level;

determining a second set of face detection tests that are to be applied to the second detection window data; and

applying the second set of face detection tests to the second detection window data to determine a second confidence level value that indicates a likelihood that the particular object, of the one or more objects, is included in the second detection window.

9. The non-transitory computer-readable storage medium of claim 8, further comprising additional instructions which, when executed, cause the processors to perform:

in response to determining that the second confidence level value is greater than a second threshold value, determining that the particular object, of the one or more objects, is depicted in the second detection window.

10. The non-transitory computer-readable storage medium of claim 9, further comprising additional instructions which, when executed, cause the one or more processors to perform:

in response to determining that the second confidence level value is not greater than the second threshold value;

determining a second location by rotating the second detection window;

determining a third detection window, wherein the third detection window has the second size and is located at the second location;

determining, for the third detection window, third detection window data;

determining a third set of face detection tests that are to be applied to the third detection window data; and

applying the third set of face detection tests to the third detection window data to determine a third confidence level value that indicates a likelihood that the particular object, of the one or more objects, is included in the third detection window.

11. The non-transitory computer-readable storage medium of claim 10, further comprising additional instructions which, when executed, cause the one or more processors to perform:

in response to determining that the third confidence level value is greater than a third threshold value, determining that the particular object, of the one or more objects, is depicted in the third detection window.

12. The non-transitory computer-readable storage medium of claim 11, further comprising additional instructions which, when executed, cause the one or more processors to perform:

in response to determining that the first confidence level value is greater than the first threshold value;

determining a fourth set of face detection tests that are to be applied to the first detection window data;

wherein the fourth set of face detection tests comprises a chain of classifiers to be applied to the first detection window data to determine a skin map; and

applying the fourth set of face detection tests to the first detection window data to determine a fourth confidence level value that indicates a likelihood that the particular object, of the one or more objects, included in the first detection window, is a face.

13. The non-transitory computer-readable storage medium of claim 12, wherein the fourth set of face detection tests comprises one or more frontal face detection tests.

14. The non-transitory computer-readable storage medium of claim 13,

wherein the skin map comprises a map of values computed for each pixel of the first detection window;

and

wherein a particular value in the skin map represents a probability that a particular pixel of the first detection window depicts a portion of a human skin.

15. A device, comprising:

an image acquiring unit communicatively coupled to a memory unit, and configured to acquire data of a digital image that depicts one or more objects; and

a face detection unit configured to;

determine a first size of a first detection window and an initial location of the first detection window in the digital image;

wherein the first detection window encompasses a first portion of the digital image;

determine, for the first detection window, first detection window data that represents the first detection window at a first image resolution level;

determine a first set of face detection tests that are to be applied to the first detection window data;

apply the first set of face detection tests to the first detection window data to determine a first confidence level value that indicates a likelihood that a particular object, of the one or more objects, is included in the first detection window;

in response to determining that the first confidence level value is not greater than a first threshold value;

determine a second size that is greater than the first size;

determine a second detection window that has the second size;

wherein the second detection window encompasses a second portion of the digital image;

determine, for the second detection window, second detection window data that represents the second detection window at a second image resolution level;

determine a second set of face detection tests that are to be applied to the second detection window data; and

apply the second set of face detection tests to the second detection window data to determine a second confidence level value that indicates a likelihood that the particular object, of the one or more objects, is included in the second detection window.

16. The device of claim 15, wherein the face detection unit is further configured to:

in response to determining that the second confidence level value is greater than a second threshold value, determine that the particular object, of the one or more objects, is depicted in the second detection window.

17. The device of claim 16, wherein the face detection unit is further configured to:

in response to determining that the second confidence level value is not greater than the second threshold value;

determine a second location by rotating the second detection window;

determine a third detection window, wherein the third detection window has the second size and is located at the second location;

determine, for the third detection window, third detection window data;

determine a third set of face detection tests that are to be applied to the third detection window data; and

apply the third set of face detection tests to the third detection window data to determine a third confidence level value that indicates a likelihood that the particular object, of the one or more objects, is included in the third detection window.

18. The device of claim 17, wherein the face detection unit is further configured to:

in response to determining that the third confidence level value is greater than a third threshold value, determine that the particular object, of the one or more objects, is depicted in the third detection window.

19. The device of claim 18, wherein the face detection unit is further configured to:

in response to determining that the first confidence level value is greater than the first threshold value;

determine a fourth set of face detection tests that are to be applied to the first detection window data;

wherein the fourth set of face detection tests comprises a chain of classifiers to be applied to the first detection window data to determine a skin map; and

apply the fourth set of face detection tests to the first detection window data to determine a fourth confidence level value that indicates a likelihood that the particular object, of the one or more objects, included in the first detection window, is a face.

20. The device of claim 19,

wherein the skin map comprises a map of values computed for each pixel of the first detection window;

and

wherein a particular value in the skin map represents a probability that a particular pixel of the first detection window depicts a portion of a human skin.