Demographic Analysis of Facial Landmarks
First Claim
1. A method comprising:
- obtaining a set of training vectors, wherein each training vector is mapped to either a male gender or a female gender, and wherein the training vectors represent facial landmarks derived from respective facial images;
identifying, by a computing device, an input vector of facial landmarks, wherein the facial landmarks of the input vector are derived from a particular facial image;
selecting, by the computing device, a feature vector containing a subset of the facial landmarks of the input vector, wherein selecting the feature vector comprises;
(i) constructing a graph representing the training vectors, each vertex in the graph corresponding to one of the training vectors, (ii) connecting pairs of vertices in the graph using respective uniform weight edges where the corresponding training vectors map to the same gender, and (iii) selecting the feature vector based on an adjacency matrix of the graph; and
based on a weighted comparison between the feature vector and at least some of the training vectors, classifying, by the computing device, the particular facial image as either the male gender or the female gender.
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Abstract
A set of training vectors may be identified. Each training vector may be mapped to either a male gender or a female gender, and each training vector may represent facial landmarks derived from a respective facial image. An input vector of facial landmarks may also be identified. The facial landmarks of the input vector may be derived from a particular facial image. A feature vector may containing a subset of the facial landmarks may be selected from the input vector. A weighted comparison may be performed between the feature vector and each of the training vectors. Based on a result of the weighted comparison, the particular facial image may be classified as either the male gender or the female gender.
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Citations
20 Claims
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1. A method comprising:
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obtaining a set of training vectors, wherein each training vector is mapped to either a male gender or a female gender, and wherein the training vectors represent facial landmarks derived from respective facial images; identifying, by a computing device, an input vector of facial landmarks, wherein the facial landmarks of the input vector are derived from a particular facial image; selecting, by the computing device, a feature vector containing a subset of the facial landmarks of the input vector, wherein selecting the feature vector comprises;
(i) constructing a graph representing the training vectors, each vertex in the graph corresponding to one of the training vectors, (ii) connecting pairs of vertices in the graph using respective uniform weight edges where the corresponding training vectors map to the same gender, and (iii) selecting the feature vector based on an adjacency matrix of the graph; andbased on a weighted comparison between the feature vector and at least some of the training vectors, classifying, by the computing device, the particular facial image as either the male gender or the female gender. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An article of manufacture including a non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing device, cause the computing device to perform operations comprising:
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obtaining a set of training vectors, wherein each training vector is mapped to either a male gender or a female gender, and wherein the training vectors represent facial landmarks derived from respective facial images; identifying an input vector of facial landmarks, wherein the facial landmarks of the input vector are derived from a particular facial image; selecting a feature vector containing a subset of the facial landmarks of the input vector, wherein selecting the feature vector comprises;
(i) constructing a graph representing the training vectors, each vertex in the graph corresponding to one of the training vectors, (ii) connecting pairs of vertices in the graph using respective uniform weight edges where the corresponding training vectors map to the same gender, and (iii) selecting the feature vector based on an adjacency matrix of the graph; andbased on a weighted comparison between the feature vector and at least some of the training vectors, classifying the particular facial image as either the male gender or the female gender. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A computing system comprising:
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at least one processor; memory; and program instructions, stored in the memory, that upon execution by the processor cause the computing system to perform operations including; obtaining a set of training vectors, wherein each training vector is mapped to either a male gender or a female gender, and wherein the training vectors represent facial landmarks derived from respective facial images; identifying an input vector of facial landmarks, wherein the facial landmarks of the input vector are derived from a particular facial image; selecting a feature vector containing a subset of the facial landmarks of the input vector, wherein selecting the feature vector comprises;
(i) constructing a graph representing the training vectors, each vertex in the graph corresponding to one of the training vectors, (ii) connecting pairs of vertices in the graph using respective uniform weight edges where the corresponding training vectors map to the same gender, and (iii) selecting the feature vector based on an adjacency matrix of the graph; andbased on a weighted comparison between the feature vector and at least some of the training vectors, classifying the particular facial image as either the male gender or the female gender. - View Dependent Claims (18, 19, 20)
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Specification