VGG16

The VGG-Face network model is made publicly available by the Visual Geometry Group at Oxford University. Involving almost 135 million trainable parameters, this network has been shown to achieve a FR accuracy of 98.95% on the LFW unrestricted. VGG-Face is a CNN consisting of 16 hidden layers. The initial 13 hidden layers are convolution and pooling layers, and the last three layers are fully-connected (‘fc6’, ‘fc7’, and ‘fc8’). The input to this network is an appropriately cropped color face-image of pre-specified dimensions.

We use the representation produced by the ‘fc7’ layer of the VGG-Face CNN as a template for the input image. When enrolling a client, the template produced by the VGG-Face network for each enrollment-sample is recorded. For scoring, the network is used to generate a template for the probe face-image, which is then compared to the enrolled templates of the claimed identity using the Cosine-similarity measure.

Check it out https://www.idiap.ch/software/bob/docs/bob/bob.bio.caffe_face/stable/index.html for more information.