  Robust face representation is imperative to highly accurate face recognition.
In this work, we propose an open source face recognition method with deep
representation named as VIPLFaceNet, which is a 10-layer deep convolutional
neural network with 7 convolutional layers and 3 fully-connected layers.
Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training
time and 60% testing time, but achieves 40\% drop in error rate on the
real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean
accuracy on LFW using one single network. An open-source C++ SDK based on
VIPLFaceNet is released under BSD license. The SDK takes about 150ms to process
one face image in a single thread on an i7 desktop CPU. VIPLFaceNet provides a
state-of-the-art start point for both academic and industrial face recognition
applications.
