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.

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.

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.

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.

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.

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.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

Abstract: In multilevel clustering for a face recognition process, the first stage clustering is performed on each computing node, using the first x vector coefficients. From the resulting k clusters created in the first stage, a limited number of clusters are selected on which the second stage clustering is performed, using the next y vector coefficients. The search for a matching image is then limited to these selected clusters. Computational costs are reduced at the first stage clustering by using just the first x vector coefficients. Computational costs for the second stage clustering are also reduced by performing the second stage only with the limited number of clusters on a limited number of computing nodes. In this manner, the overall computational costs in the face recognition process is significantly reduced while maintaining a desired level of accuracy.

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.

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.