Demographic analysis of facial landmarks

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:

• 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, (iii) determining a training matrix representing the training vectors, (iv) determining a covariance matrix of the training matrix, (v) using a random forest technique to build a plurality of trees, wherein each node of each tree in the plurality of trees represents a random selection of the facial landmarks, (vi) calculating the Gini importance of the facial landmarks and (vii) selecting the feature vector based on an adjacency matrix of the graph and the Gini importance of the facial landmarks;
• and
• based 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.

A computing system comprising:

• 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, (iii) determining a training matrix representing the training vectors, (iv) determining a covariance matrix of the training matrix, (v) using a random forest technique to build a plurality of trees, wherein each node of each tree in the plurality of trees represents a random selection of the facial landmarks, (vi) calculating the Gini importance of the facial landmarks and (vii) selecting the feature vector based on an adjacency matrix of the graph and the Gini importance of the facial landmarks;
• and
• based 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.