Face recognition in big data ecosystem using multiple recognition models
First Claim
1. A method of training a facial recognition modeling system using an extremely large data set of facial images, the method comprising:
- distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system;
optimizing a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models;
calculating an eigenvector distance between a dataset facial image within the extremely large data set of facial images and a most closely matching facial image within each of the plurality of facial recognition models;
determining a least closely matching facial image associated with a maximum eigenvector distance between the dataset facial image and each of the most closely matching facial images; and
inserting the dataset facial image into the facial recognition model associated with the least closely matching facial image.
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Abstract
A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.
18 Citations
9 Claims
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1. A method of training a facial recognition modeling system using an extremely large data set of facial images, the method comprising:
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distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system; optimizing a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models; calculating an eigenvector distance between a dataset facial image within the extremely large data set of facial images and a most closely matching facial image within each of the plurality of facial recognition models; determining a least closely matching facial image associated with a maximum eigenvector distance between the dataset facial image and each of the most closely matching facial images; and inserting the dataset facial image into the facial recognition model associated with the least closely matching facial image. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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Specification