Deep Face Model Compression Using Entropy-Based Filter Selection

The state-of-the-art face recognition systems are built on deep convolutional neural networks (CNNs). However, these CNNs contain millions of parameters, leading to the deployment difficulties on mobile and embedded devices. One solution is to reduce the size of the trained CNNs by model compression. In this work, we propose an entropy-based prune metric to reduce the size of intermediate activations so as to accelerate and compress CNN models both in training and inference stages. First the importance of each filter in each layer is evaluated by our entropy-based method. Then some unimportant filters are removed according to a predefined compressing rate. Finally, we fine-tune the pruned model to improve its discrimination ability. Experiments conducted on LFW face dataset shows the effectiveness of our entropy-based method. We achieve \(1.92\times \) compression and \(1.88\times \) speed-up on VGG-16 model, \(2\times \) compression and \(1.74\times \) speed-up on WebFace model, both with only about 1% accuracy decrease evaluated on LFW.