  In order to make facial features more discriminative, some new models have
recently been proposed. However, almost all of these models use the traditional
face verification method, where the cosine operation is performed using the
features of the bottleneck layer output. However, each of these models needs to
change a threshold each time it is operated on a different test set. This is
very inappropriate for application in real-world scenarios. In this paper, we
train a validation classifier to normalize the decision threshold, which means
that the result can be obtained directly without replacing the threshold. We
refer to our model as validation classifier, which achieves best result on the
structure consisting of one convolution layer and six fully connected layers.
To test our approach, we conduct extensive experiments on Labeled Face in the
Wild (LFW) and Youtube Faces (YTF), and the relative error reduction is 25.37%
and 26.60% than traditional method respectively. These experiments confirm the
effectiveness of validation classifier on face recognition task.
