免注册Face-SSD：Single Shot分析微笑，面部属性，并在野外影响
Registration-free Face-SSD: Single shot analysis of smiles, facial attributes, and affect in the wild
摘要：
在本文中，我们提出了一种新的Single Shot人脸相关的任务分析方法，称为Face-SSD，用于检测人脸和执行各种人脸相关的（分类/回归）任务，包括微笑识别，人脸属性预测和效价唤醒估计在野外。Face-SSD使用全卷积神经网络（FCNN）来检测不同大小的多个面部，并识别/回归一个或多个面部相关类。Face-SSD有两个并行分支，共享相同的低级过滤器，一个分支处理人脸检测，另一个分支处理人脸分析任务。两个分支的输出是并行产生的空间对齐的热图-因此Face-SSD不需要在后续步骤中执行面部检测，面部区域提取，尺寸归一化和面部区域处理。我们的贡献有三个方面：1）Face-SSD是第一个在不依赖于预处理（如人脸检测和注册）的情况下执行人脸分析的网络- Face-SSD是一个简单的单一FCNN架构，同时执行人脸检测和人脸相关任务分析-这些任务通常被视为单独的连续任务; 2）Face-SSD是适用于各种面部分析任务而不修改网络结构的通用架构-这与设计特定于任务的架构相反;和3）Face-SSD即使在检测多个人脸和识别给定图像中的多个类别时也能实现实时性能（21 FPS）。实验结果表明，Face-SSD在各种人脸分析任务中实现了最先进的性能，微笑检测的识别准确率为95.76%，属性预测为90.29%，效价和唤醒估计的均方根（RMS）误差为0.44和0.39。
英文摘要：
In this paper, we present a novel single shot face-related task analysis method, called Face-SSD, for detecting faces and for performing various face-related (classification/regression) tasks including smile recognition, face attribute prediction and valence-arousal estimation in the wild. Face-SSD uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of different sizes and recognise/regress one or more face-related classes. Face-SSD has two parallel branches that share the same low-level filters, one branch dealing with face detection and the other one with face analysis tasks. The outputs of both branches are spatially aligned heatmaps that are produced in parallel - therefore Face-SSD does not require that face detection, facial region extraction, size normalisation, and facial region processing are performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is the first network to perform face analysis without relying on pre-processing such as face detection and registration in advance - Face-SSD is a simple and a single FCNN architecture simultaneously performing face detection and face-related task analysis - those are conventionally treated as separate consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable for various face analysis tasks without modifying the network structure - this is in contrast to designing task-specific architectures; and 3) Face-SSD achieves real-time performance (21 FPS) even when detecting multiple faces and recognising multiple classes in a given image. Experimental results show that Face-SSD achieves state-of-the-art performance in various face analysis tasks by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for valence and arousal estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
