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.
Abstract (translated)
随着电子设备的广泛普及，与传统的密码和模式解锁相比，生物特征识别技术的出现给用户认证带来了极大的便利。在众多生物学特征中，人脸是一个普遍的、不可替代的特征，不需要太多的合作，同时可以显著改善用户体验。人脸识别是电子设备宣传的主要功能之一。因此，在计算机视觉领域的研究是非常有价值的。该领域的前期工作主要集中在两个方向：转换损失函数以提高传统深卷积神经网络（resnet）的识别精度；将最新的损失函数与轻量级系统（mobilenet）相结合，以最小的精度代价减小网络大小。但这些都没有改变网络结构。随着automl技术的发展，神经网络结构搜索（nas）在图像分类基准中显示出了良好的性能。在本文中，我们将NAS技术集成到人脸识别中，以定制一个更合适的网络。提出了一种交替训练子网络和控制器网络的神经结构搜索框架。同时，我们将评估延迟纳入强化学习的奖励，并利用策略梯度算法自动搜索具有最经典交叉熵损失的架构。我们所研究的网络结构在大规模人脸数据集中具有最先进的准确性，在MS-CELEB-1M中达到98.77%的前1位，在LFW中达到99.89%，网络规模相对较小。据我们所知，这是第一次尝试使用NAS来解决深层人脸识别问题，并在这一领域取得最佳效果。
URL
https://arxiv.org/abs/1904.09523
