Neural Architecture Search for Deep Face Recognition

Abstract

By the widespread popularity of electronic devices, the emergence of biometric technology has brought significant convenience to user authentication compared with the traditional password and mode unlocking. Among many biological characteristics, the face is a universal and irreplaceable feature that does not need too much cooperation and can significantly improve the user's experience at the same time. Face recognition is one of the main functions of electronic equipment propaganda. Hence it's virtually worth researching in computer vision. Previous work in this field has focused on two directions: converting loss function to improve recognition accuracy in traditional deep convolution neural networks (Resnet); combining the latest loss function with the lightweight system (MobileNet) to reduce network size at the minimal expense of accuracy. But none of these has changed the network structure. With the development of AutoML, neural architecture search (NAS) has shown excellent performance in the benchmark of image classification. In this paper, we integrate NAS technology into face recognition to customize a more suitable network. We quote the framework of neural architecture search which trains child and controller network alternately. At the same time, we mutate NAS by incorporating evaluation latency into rewards of reinforcement learning and utilize policy gradient algorithm to search the architecture automatically with the most classical cross-entropy loss. The network architectures we searched out have got state-of-the-art accuracy in the large-scale face dataset, which achieves 98.77% top-1 in MS-Celeb-1M and 99.89% in LFW with relatively small network size. To the best of our knowledge, this proposal is the first attempt to use NAS to solve the problem of Deep Face Recognition and achieve the best results in this domain.

References in corpus (15)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• DARTS: Differentiable Architecture Search
• DeepID3: Face Recognition with Very Deep Neural Networks
• Efficient Neural Architecture Search via Parameter Sharing
• DropBlock: A regularization method for convolutional networks
• SphereFace: Deep Hypersphere Embedding for Face Recognition
• Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
• SNAS: Stochastic Neural Architecture Search
• Targeting Ultimate Accuracy: Face Recognition via Deep Embedding
• Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
• Range Loss for Deep Face Recognition with Long-tail
• sharpDARTS: Faster and More Accurate Differentiable Architecture Search
• A Performance Comparison of Loss Functions for Deep Face Recognition
• BlockQNN: Efficient Block-wise Neural Network Architecture Generation

Cited by in corpus (2)

• Searching Central Difference Convolutional Networks for Face Anti-Spoofing
• A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices