A face recognition algorithm to compare one probe image against a set of template images.

This algorithm is a sequential one. The platform will call its process() method once per data incoming on its inputs.

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: main

Endpoint Name Data Format Nature
probe_image system/array_2d_uint8/1 Input
template_images system/array_3d_uint8/1 Input
score system/float/1 Output

Group: model

Endpoint Name Data Format Nature
model_pb system/text/1 Input

The code for this algorithm in Python
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A face recognition algorithm to compare one probe image against a set of template images. The images must be gray-scale and should contain the face region only. Internally, the images are resized to 160x160 pixels. This algorithm expects the pre-trained FaceNet model to be provided as input as well. The model can be downloaded from https://drive.google.com/file/d/0B5MzpY9kBtDVZ2RpVDYwWmxoSUk which was made available in https://github.com/davidsandberg/facenet/tree/b95c9c3290455cabc425dc3f9435650679a74c50


Updated Name Databases/Protocols Analyzers
amohammadi/amohammadi/atnt_facenet/1/atnt_facenet_1 atnt/5@idiap,facenet-20170512-110547/1@facenet-20170512-110547 amohammadi/eer_analyzer/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

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