System and Method for Detecting and Tracking Features in Images
First Claim
1. A method for detecting and tracking a feature in an image, comprising the steps of:
- obtaining a set of training images clustered into clustered shape subspaces representative of at least one non-linear shape manifold in the training images;
receiving an image in which identification of a feature in the image is desired and movement of the feature has occurred with respect to a previous image;
creating an initial shape for identifying the feature in the image;
searching through the clustered shape subspaces to find a potential matching shape which potentially matches the feature;
deforming the initial shape into the potential matching shape; and
continuing searching and deformation until a final shape indicative of the feature is obtained.
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Abstract
A system and method for tracking features, e.g., facial features, is provided, which allows for the tracking of features which move in a series of images and whose shape changes nonlinearly due to perspective projection and complex 3D movements. A training set of images is processed to produce clustered shape subspaces corresponding to the set of images, such that non-linear shape manifolds in the images are represented as piecewise, overlapping linear surfaces that are clustered according to similarities in perspectives. A landmark-based training algorithm (e.g., ASM) is applied to the clustered shape subspaces to train a model of the clustered shape subspaces and to create training data. A subsequent image is processed using the training data to identify features in the target image by creating an initial shape, superimposing the initial shape on the target image, and then iteratively deforming the shape in accordance with the model until a final shape is produced corresponding to a feature in the target image.
60 Citations
25 Claims
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1. A method for detecting and tracking a feature in an image, comprising the steps of:
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obtaining a set of training images clustered into clustered shape subspaces representative of at least one non-linear shape manifold in the training images; receiving an image in which identification of a feature in the image is desired and movement of the feature has occurred with respect to a previous image; creating an initial shape for identifying the feature in the image; searching through the clustered shape subspaces to find a potential matching shape which potentially matches the feature; deforming the initial shape into the potential matching shape; and continuing searching and deformation until a final shape indicative of the feature is obtained. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A method for detecting movement of a subject'"'"'s head in a series of images, comprising the steps of:
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identifying a feature in a set of images corresponding to a subject'"'"'s nosetip; determining coordinates of the feature for each frame of the set of images; determining whether at least one value of the coordinates differs across the set of images; and if at least one value differs, indicating that the subject'"'"'s head has moved. - View Dependent Claims (22, 23)
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24. A method for detecting blinking of a subject in a series of images, comprising the steps of.
detecting a feature in a set of images corresponding to a subject'"'"'s eye; -
comparing the feature to a template of a closed eye; determining whether the detected feature matches template; and if the detected feature matches the template, indicating that a subject blinked. - View Dependent Claims (25)
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Specification