Method And Apparatus For Detecting Faces In Digital Images

• US 20080107341A1
• Filed: 11/02/2006
• Published: 05/08/2008
• Est. Priority Date: 11/02/2006
• Status: Abandoned Application

• Current Assignee: Seiko Epson Corporation (Seiko Group)
• Original Assignee: Seiko Epson Corporation (Seiko Group)
• Inventors: Lu, Juwei
• Application Number:
  US11/556,082
• Publication Number:
  US 20080107341A1
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 10/36 Applying a local operator, i.e. means to operate on image points situated in the vicinity of a given point; Non-linear local filtering operations, e.g. median filtering
• CPC Class Codes: G06V 10/446 using Haar-like filters, e.g. using integral image techniques
• CPC Class Codes: G06V 10/507 Summing image-intensity values; Histogram projection analysis
• CPC Class Codes: G06V 10/774 Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06V 40/165 using facial parts and geometric relationships

Abstract

A method of detecting faces in a digital image comprises selecting a sub-window of the digital image. Sample regions of the sub-window are then selected. The sample regions are analyzed to determine if the sub-window likely represents a face.

Claims

1. A method of detecting faces in a digital image, comprising:

selecting a sub-window of said digital image;

selecting sample regions in said sub-window; and

analyzing said sample regions to determine if said sub-window likely represents a face.

2. The method of claim 1, wherein said sample regions are rectangular.

3. The method of claim 1, further comprising:

prior to selecting said sample regions, dividing said sub-window into frames, each of said sample regions being located in a different one of said frames.

4. The method of claim 3, wherein said sample regions are offset from the borders of said frames.

5. The method of claim 3, wherein said sub-window is divided into at least four frames.

6. The method of claim 1, wherein said sample regions are in pre-determined locations in said sub-window.

7. The method of claim 1, wherein said sample regions form a pattern.

8. The method of claim 7, wherein said pattern is a grid pattern.

9. The method of claim 1, wherein said analyzing comprises processing said features using an AdaBoost approach.

10. The method of claim 1, further comprising:

panning said sub-window across said image; and

for each position of said sub-window within said image, re-performing said selecting and analyzing.

11. The method of claim 10, further comprising:

after the sub-window has been panned across said image, adjusting the scale of said sub-window, and repeating said panning and said re-performing.

12. The method of claim 11 wherein said adjusting is re-performed until said sub-window is scaled to a minimum threshold size.

13. The method of claim 11, wherein during said analyzing, said sample regions are subjected to a series of processing stages to detect and confirm the existence of a face in said sub-window.

14. The method of claim 13, wherein said processing stages at least comprise skin color classification, edge magnitude classification and Adaboost classification.

15. The method of claim 14 wherein skin color classification is used to detect the existence of a face in said sub-window.

16. The method of claim 15 wherein edge magnitude classification is used to confirm the existence of the face in said sub-window.

17. The method of claim 16 wherein AdaBoost classification is used to reconfirm the existence of the face in said sub-window.

18. An apparatus for detecting faces in a digital image, comprising:

a sub-window selector selecting a sub-window in said digital image;

a sample region selector selecting sample regions within said sub-window; and

a sample region analyzer analyzing said sample regions to determine if said sub-window likely represents a face.

19. An apparatus according to claim 18, wherein said sample region selector divides said sub-window into frames, and selects each of said sample regions from a different one of said frames.

20. An apparatus according to claim 19, wherein said sample regions are offset from the borders of said frames.

21. An apparatus according to claim 18, wherein said sample regions are in pre-determined locations in said sub-window.

22. An apparatus according to claim 19, wherein said sample regions are selected in a pattern.

23. An apparatus according to claim 22, wherein said pattern is a grid pattern.

24. An apparatus according to claim 19 wherein said sample region analyzer subjects said sample regions to a series of processing stages to detect and confirm the existence of a face in said sub-window.

25. An apparatus according to claim 24 wherein said series of processing stages comprises at least two of skin color classification, edge magnitude classification and AdaBoost classification.

26. A method of detecting faces in a digital image, comprising:

selecting a sub-window of said digital image;

selecting areas of said sub-window;

dividing said areas of said sub-window into two-dimensional arrays of frames;

analyzing said two-dimensional arrays of frames to generate a feature for each said area; and

determining, using said features, if said sub-window likely represents a face.

27. The method of claim 26, wherein said areas are divided into at least four frames.

28. The method of claim 27, wherein said analyzing comprises:

thresholding characteristics of pixels of each of said frames to generate a binary map for each frame; and

generating said features by performing a function on the sums of said binary map for each frame.

29. The method of claim 28, wherein said characteristics are pixel intensities.

30. The method of claim 28, wherein said characteristics are color values of said pixels.

31. The method of claim 28, wherein said characteristics are edge magnitude values of said pixels.