Face authentication using recognition-by-parts, boosting, and transduction
First Claim
Patent Images
1. A robust recognition-by-parts face authentication system for determining if at least one query image obtained using an imaging device matches at least one training image in an enrollment gallery comprising:
- a. an enrollment module comprising;
1) an enrollment patch extractor configured for extracting a multitude of training patches at different scales for each center position of the training image;
2) an enrollment patch processor configured for;
a) selecting the training patches that are predictive in identifying the training image; and
b) reducing the selected training patches'"'"' dimensionality using transduction;
3) an enrollment part clustering module configured for clustering the selected training patches into training exemplar-based parts for matching and authentication using K-means;
4) an enrollment data fusion module configured for enrolling the training exemplar-based parts using boosting and transduction;
b. a query module comprising;
1) a query patch extractor configured for extracting a multitude of query patches at different scales for each center position of the query image;
2) a query patch processor configured for;
a) selecting the query patches that are predictive in identifying the query target; and
b) reducing the selected query patches'"'"' dimensionality using transduction;
3) a query part clustering module configured for clustering the selected query patches into query exemplar-based parts for matching and authentication using K-means; and
c. an ID authentication module configured for matching the query exemplar-based parts against a gallery of all the enrolled training exemplar-based parts using flexible matching.
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Abstract
A robust recognition-by-parts authentication system for comparing and authenticating a test image with at least one training image is disclosed. This invention applies the concepts of recognition-by-parts, boosting, and transduction.
91 Citations
18 Claims
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1. A robust recognition-by-parts face authentication system for determining if at least one query image obtained using an imaging device matches at least one training image in an enrollment gallery comprising:
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a. an enrollment module comprising; 1) an enrollment patch extractor configured for extracting a multitude of training patches at different scales for each center position of the training image; 2) an enrollment patch processor configured for; a) selecting the training patches that are predictive in identifying the training image; and b) reducing the selected training patches'"'"' dimensionality using transduction; 3) an enrollment part clustering module configured for clustering the selected training patches into training exemplar-based parts for matching and authentication using K-means; 4) an enrollment data fusion module configured for enrolling the training exemplar-based parts using boosting and transduction; b. a query module comprising; 1) a query patch extractor configured for extracting a multitude of query patches at different scales for each center position of the query image; 2) a query patch processor configured for; a) selecting the query patches that are predictive in identifying the query target; and b) reducing the selected query patches'"'"' dimensionality using transduction; 3) a query part clustering module configured for clustering the selected query patches into query exemplar-based parts for matching and authentication using K-means; and c. an ID authentication module configured for matching the query exemplar-based parts against a gallery of all the enrolled training exemplar-based parts using flexible matching. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A robust recognition-by-parts face authentication device for determining if at least one query image obtained using an imaging device matches at least one training image in an enrollment gallery comprising:
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a. an enrollment module comprising; 1) an enrollment patch extractor configured for extracting a multitude of training patches at different scales for each center position of the training image; 2) an enrollment patch processor configured for; a) selecting the training patches that are predictive in identifying the training image; and b) reducing the selected training patches'"'"' dimensionality using transduction; 3) an enrollment part clustering module configured for clustering the selected training patches into training exemplar-based parts for matching and authentication using K-means; 4) an enrollment data fusion module configured for enrolling the training exemplar-based parts using boosting and transduction; b. a query module comprising; 1) a query patch extractor configured for extracting a multitude of query patches at different scales for each center position of the query image; 2) a query patch processor configured for; a) selecting the query patches that are predictive in identifying the query target; and b) reducing the selected query patches'"'"' dimensionality using transduction; 3) a query part clustering module configured for clustering the selected query patches into query exemplar-based parts for matching and authentication using K-means; and c. an ID authentication module configured for matching the query exemplar-based parts against a gallery of all the enrolled training exemplar-based parts using flexible matching. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A physical and tangible computer readable medium encoded with instructions for determining if at least one query image obtained using an imaging device matches at least one training image in an enrollment gallery, wherein execution of the instructions by one or more processors causes the one or more processors to perform the steps comprising:
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a. extracting a multitude of training patches at different scales for each center position of the training image; b. processing the training patches by; 1) selecting the training patches that are predictive in identifying the training image; and 2) reducing the selected training patches'"'"' dimensionality using transduction; c. clustering the selected training patches into training exemplar-based parts for matching and authentication using K-means; d. enrolling the training exemplar-based parts using boosting and transduction; e. extracting a multitude of query patches at different scales for each center position of the query image; f. processing the query patches by; 1) selecting the query patches that are predictive in identifying the query target; and 2) reducing the selected query patches'"'"' dimensionality using transduction; g. clustering the selected query patches into query exemplar-based parts for matching and authentication using K-means; and h. matching the query exemplar-based parts against a gallery of all the enrolled training exemplar-based parts using flexible matching. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification