VIPLFaceNet: An Open Source Deep Face Recognition SDK
This provides an open-source, efficient solution for academic and industrial face recognition applications, though it is incremental as it builds on existing deep learning methods.
The authors tackled face recognition by proposing VIPLFaceNet, a 10-layer deep convolutional neural network, which achieved 98.60% mean accuracy on the LFW benchmark with a 40% drop in error rate compared to AlexNet.
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.