US7058209B2
Expired / Lapsed
US
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Method and computer program product for locating facial features
Assignee: Monument Peak Ventures LLC
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Filing: 2001-11-26
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Granted: 2006-06-06
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Expires: 2021-11-26
Abstract
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.
Inventors
- 👤 Shoupu Chen
- 👤 Mark R. Bolin
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 11:19
CPC Classifications
G; G06; G06T; G06T7/00; G06T7/70; G06T7/73; G06T7/74; G06T7/75; G06V; G06V40/00; G06V40/10; G06V40/16; G06V40/168; G06V40/171; G06V40/18; G06V40/19; G06T2207/00; G06T2207/30; G06T2207/30216
Claims Analysis
Total Claims: 22
Claim 1
Independent Claim
130 words
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.
Claim 2
Dependent Claim
130 words
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.
Claim 3
Dependent Claim
38 words
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.
Claim 4
Dependent Claim
30 words
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.
Claim 5
Dependent Claim
127 words
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.
Claim 6
Dependent Claim
127 words
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.
Claim 7
Dependent Claim
35 words
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.
Claim 8
Dependent Claim
45 words
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.
Claim 9
Dependent Claim
25 words
The method of claim 4 , wherein spacing of search locations is automatically scaled based on a current estimate of the size of the face.
Claim 10
Dependent Claim
46 words
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.
Claim 11
Dependent Claim
40 words
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.
Claim 12
Dependent Claim
146 words
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.
Claim 13
Dependent Claim
146 words
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.
Claim 14
Dependent Claim
40 words
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.
Claim 15
Dependent Claim
32 words
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.
Claim 16
Dependent Claim
143 words
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.
Claim 17
Dependent Claim
143 words
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.
Claim 18
Dependent Claim
37 words
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.
Claim 19
Dependent Claim
27 words
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.
Claim 20
Dependent Claim
46 words
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.
Claim 21
Dependent Claim
47 words
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.
Claim 22
Dependent Claim
42 words
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.
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