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.

本文提出了一种名为 Face-SSD 的新颖单次快照人脸相关任务分析方法，使用完全卷积神经网络检测各种规模的多个面孔，并识别 / 回归一个或多个面部相关类别。Face-SSD 是第一个不依赖于预处理（如提前脸部检测和配准）即可执行人脸分析的网络，具有实时性能，同时可以检测多个面孔并识别多个类别，为各种面部分析任务提供了具有广泛适应性的架构，并在各项面部分析任务中实现了最先进的性能。

无需登记的人脸 SSD：野外微笑、面部属性和情感的单次拍摄分析

Registration-free Face-SSD: Single shot analysis of smiles, facial  attributes, and affect in the wild

Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to the size of databases for face recognition, far less
labeled data is available for training smile detection systems. To leverage the
large amount of labeled data from face recognition datasets and to alleviate
overfitting on smile detection, an efficient transfer learning-based smile
detection approach is proposed in this paper. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper.

本文提出了一种使用迁移学习的微笑检测方法，利用经过训练的深度人脸识别模型，并在不同的输入下 fine-tune 该模型，实现对人脸图像中微笑的检测。实验证明，该方法取得了最先进的性能。