Released in 2016, these models tackle the problem of the pose and viewpoint variations in facial recognition systems. Unlike other models that attempt to transform different poses and viewpoints to a canonical frontal pose, this set of models provides multiple pose-specific nets.
Number of models: 10
Examples
Resource retrieval
Get the pre-trained net:
NetModel parameters
This model consists of a family of individual nets, each identified by a specific parameter combination. Inspect the available parameters:
Pick a non-default net by specifying the parameters:
Pick a non-default uninitialized net:
Evaluation function
Create an evaluation function that takes two facial images and outputs True if they belong to the same person and False if not:
Basic usage
Predict whether two facial images belong to the same person or not using the evaluation function:
Net information
Inspect the number of parameters of all arrays in the net:
Obtain the total number of parameters:
Obtain the layer type counts:
Export to MXNet
Export the net into a format that can be opened in MXNet:
Export also creates a net.params file containing parameters:
Get the size of the parameter file:
Requirements
Wolfram Language 12.0
(April 2019) or above
Resource History
Reference