[论文解读] Neural Architecture Search for Deep Face Recognition

Ning Zhu|arXiv (Cornell University)|Apr 21, 2019

Face recognition and analysis参考文献 41被引用 10

一句话总结

该论文提出了一种专为深度人脸识别设计的新型神经架构搜索（NAS）框架，采用具有延迟感知奖励的强化学习方法，自动发现高效且高精度的网络架构。该方法在MS-Celeb-1M数据集上实现了98.77%的top-1准确率，在LFW数据集上达到99.89%的准确率，且模型参数量仅最多19.1M，是首次成功将NAS应用于人脸识别并取得优越结果的尝试。

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.

研究动机与目标

• 探究神经架构搜索（NAS）是否能够自动为深度人脸识别设计出更优的神经网络架构。
• 解决人脸识别系统中模型精度与推理延迟之间的权衡问题。
• 通过优化网络架构本身，超越传统人工设计的网络（如ResNet和MobileNet）在人脸识别中的性能表现。
• 将延迟因素整合到NAS的奖励函数中，以在真实部署场景下平衡精度与效率。

提出的方法

• 该方法采用基于强化学习的NAS框架，其中控制器网络负责生成子网络架构。
• 搜索过程交替进行子网络训练与控制器参数更新，采用策略梯度优化方法。
• 设计了一种自定义奖励函数，将交叉熵损失与推理延迟相结合，以在架构搜索过程中平衡精度与效率。
• 搜索空间包含深度可分离卷积和标准卷积模块，这些是轻量化CNN的典型结构。
• 控制器使用指针网络生成架构操作，实现在离散架构选择空间中的有效搜索。
• 最终架构在大规模人脸数据集上使用标准交叉熵损失从头开始训练。

实验结果

研究问题

• RQ1 NAS能否有效应用于深度人脸识别，以发现超越人工设计模型的架构？
• RQ2 在NAS奖励函数中引入推理延迟后，对精度与模型效率之间的权衡有何影响？
• RQ3 与使用标准主干网络相比，专门针对人脸识别进行架构搜索能带来多大性能提升？
• RQ4 NAS能否生成适合在移动设备和边缘设备上部署的紧凑且高精度模型？

主要发现

• NAS优化后的架构在大规模MS-Celeb-1M数据集上实现了98.77%的top-1准确率，超越了此前的SOTA方法。
• 在LFW基准测试中，该模型达到了99.89%的准确率，创下人脸识别的新SOTA记录。
• 表现最佳的架构仅包含1910万参数，展现出在保持高精度的同时具有极高的效率。
• 将延迟整合到奖励函数中，使得生成的架构在不损失识别性能的前提下实现了更快的推理速度。
• 该方法是首个将NAS应用于深度人脸识别的工作，证实了其在自动、任务特定架构设计方面的潜力。
• 结果表明，NAS能够发现高效且紧凑的架构，在人脸识别任务中优于标准的ResNet和MobileNet变体。