Method and computer program product for locating facial features

• US 7,058,209 B2
• Filed: 11/26/2001
• Issued: 06/06/2006
• Est. Priority Date: 09/20/2001
• Status: Active Grant

• Current Assignee: Monument Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: Eastman Kodak Company
• Inventors: Chen, Shoupu, Bolin, Mark R.
• Primary Examiner(s): Ahmed, Samir
• Assistant Examiner(s): KIM, CHONG R
• Application Number:
  US09/994,096
• Publication Number:
  US 20030053663A1
• Time in Patent Office: 1,653 Days
• Field of Search: 382/115, 382/117, 382/118, 382/225, 382/291
• US Class Current: 382/117
• CPC Class Codes: G06T 2207/30216 Redeye defect
• CPC Class Codes: G06T 7/74 involving reference images or patches
• CPC Class Codes: G06T 7/75 involving models
• 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: G06V 40/19 Sensors therefor

Abstract

A digital image processing method detects facial features in a digital image. This method includes the steps of detecting iris pixels in the image, clustering the iris pixels, and selecting at least one of the following schemes to identify eye positions: applying geometric reasoning to detect eye positions using the iris pixel clusters; applying a summation of squared difference method using the iris pixel clusters to detect eye positions; and applying a summation of squared difference method to detect eye positions from the pixels in the image. The method applied to identify eye positions is selected on the basis of the number of iris pixel clusters, and the facial features are located using the identified eye positions.

Claims

1. A digital image processing method for detecting facial features in a digital image, comprising the steps of:

detecting iris pixels;

clustering the iris pixels;

selecting at least one of the following methods to identify eye positions in an image;

i) applying geometric reasoning to detect eye positions using the iris pixel clusters;

ii) applying a summation of squared difference method to detect eye positions based upon the iris pixel clusters; and

,

iii) applying a summation of squared difference method to detect eye positions from the pixels in the image;

wherein the method applied is selected on the basis of the number of iris pixel clusters; and

locating facial features using identified eye positions; and

wherein estimated locations to search for the facial features are based on the identified eye positions.

2. The method of claim 1, wherein the estimated locations to search for the facial features are found by aligning the eye positions within a model of the shape of the facial features with the identified eye positions.

3. The method of claim 1, wherein estimated locations to search for the facial features are based on the average position of these features within a set of example faces.

4. A digital image processing method for detecting facial features in a digital image, comprising the steps of:

detecting iris pixels;

clustering the iris pixels;

selecting at least one of the following methods to identify eye positions in an image;

i) applying geometric reasoning to detect eye positions using the iris pixel clusters;

ii) applying a summation of squared difference method to detect eye positions based upon the iris pixel clusters; and

,

iii) applying a summation of squared difference method to detect eye positions from the pixels in the image;

wherein the method applied is selected on the basis of the number of iris pixel clusters; and

locating facial features using identified eye positions; and

wherein the facial feature positions are identified using an active shape model technique.

5. The method of claim 4, wherein the shape model technique uses texture windows and the size of the texture windows is automatically scaled based on a current estimate of the size of the face.

6. The method of claim 5 wherein an estimate of the size of the face is found by determining a scale that best aligns a current estimate of the feature positions with a model of average positions of the facial features using a least squares process.

7. The method of claim 4, wherein spacing of search locations is automatically scaled based on a current estimate of the size of the face.

8. The method of claim 7 wherein an estimate of the size of the face is found by determining the scale that best aligns a current estimate of the feature positions with a model of the average positions of the facial features using a least squares process.

9. The method of claim 4, wherein the positions of the facial features that are outside a shape space boundary are constrained to locations of a shape found at a nearest point on a hyper-elliptical boundary of the shape space.

10. A computer program product for detecting facial features in a digital image, the computer program product comprising a computer readable storage medium having a computer program stored thereon for performing the steps of:

detecting iris pixels;

clustering the iris pixels;

selecting at least one of the following methods to identify eye positions in the image;

i) applying geometric reasoning to detect eye positions using the iris pixel clusters;

ii) applying a summation of squared difference method to detect eye positions based upon the iris pixel clusters; and

iii) applying a summation of squared difference method to detect eye positions from the pixels in the image;

wherein the method applied is selected on the basis of the number of iris pixel clusters; and

locating facial features using identified eye positions; and

wherein estimated locations to search for the facial features are based on the identified eye positions.

11. The computer program product of claim 10, wherein the estimated locations to search for the facial features are found by aligning the eye positions within a model of the shape of the facial features with the identified eye positions.

12. The computer program product of claim 10, wherein estimated locations to search for the facial features are based on an average position of these features within a set of example faces.

13. A computer program product for detecting facial features in a digital image, the computer program product comprising a computer readable storage medium having a computer program stored thereon for performing the steps of:

detecting iris pixels;

clustering the iris pixels;

selecting at least one of the following methods to identify eye positions in the image;

i) applying geometric reasoning to detect eye positions using the iris pixel clusters;

ii) applying a summation of squared difference method to detect eye positions based upon the iris pixel clusters; and

iii) applying a summation of squared difference method to detect eye positions from the pixels in the image;

wherein the method applied is selected on the basis of the number of iris pixel clusters; and

locating facial features using identified eye positions; and

wherein the facial feature positions are identified using an active shape model technique.

14. The computer program product of claim 13, wherein the shape model technique uses texture windows and the size of the texture windows is automatically scaled based on a current estimate of the size of the face.

15. The computer program product of claim 13, wherein spacing of search locations is automatically scaled based on a current estimate of the size of the face.

16. The computer program product of claim 15 wherein an estimate of the size of the face is found by determining scale that best aligns a current estimate of the feature positions with a model of average positions of the facial features using a least squares process.

17. The computer program product of claim 14 wherein an estimate of the size of the face is found by determining a scale that best aligns a current estimate of the feature positions with a model of average positions of the facial features using a least squares process.

18. The computer program product of claim 13, wherein the positions of the facial features that are outside a shape space boundary are constrained to locations of a shape found at a nearest point on a hyperelliptical boundary of the shape space.