Recognizing faces and their underlying emotions is an important aspect of
biometrics. In fact, estimating emotional states from faces has been tackled
from several angles in the literature. In this paper, we follow the novel route
of using neuromorphic data to predict valence and arousal values from faces.
Due to the difficulty of gathering event-based annotated videos, we leverage an
event camera simulator to create the neuromorphic counterpart of an existing
RGB dataset. We demonstrate that not only training models on simulated data can
still yield state-of-the-art results in valence-arousal estimation, but also
that our trained models can be directly applied to real data without further
training to address the downstream task of emotion recognition. In the paper we
propose several alternative models to solve the task, both frame-based and
video-based.

使用神经形态学数据从面部预测价值和唤醒值，并将训练模型直接应用于情绪识别任务。

神经形态学价值和唤起估计

Neuromorphic Valence and Arousal Estimation

The objective of this work is to learn a compact embedding of a set of
descriptors that is suitable for efficient retrieval and ranking, whilst
maintaining discriminability of the individual descriptors. We focus on a
specific example of this general problem -- that of retrieving images
containing multiple faces from a large scale dataset of images. Here the set
consists of the face descriptors in each image, and given a query for multiple
identities, the goal is then to retrieve, in order, images which contain all
the identities, all but one, \etc
To this end, we make the following contributions: first, we propose a CNN
architecture -- {\em SetNet} -- to achieve the objective: it learns face
descriptors and their aggregation over a set to produce a compact fixed length
descriptor designed for set retrieval, and the score of an image is a count of
the number of identities that match the query; second, we show that this
compact descriptor has minimal loss of discriminability up to two faces per
image, and degrades slowly after that -- far exceeding a number of baselines;
third, we explore the speed vs.\ retrieval quality trade-off for set retrieval
using this compact descriptor; and, finally, we collect and annotate a large
dataset of images containing various number of celebrities, which we use for
evaluation and is publicly released.

该研究旨在学习一种紧凑的嵌入方式，该嵌入方式适用于有效的检索和排序，同时保持各个描述符的可辨别性。作者提出了一种基于 CNN 网络的框架（SetNet），以实现该目标，并展示了它在人脸图像的检索上具有很好的效果。

紧凑深度汇聚用于集合检索

Compact Deep Aggregation for Set Retrieval

We propose a novel framework, called Disjoint Mapping Network (DIMNet), for
cross-modal biometric matching, in particular of voices and faces. Different
from the existing methods, DIMNet does not explicitly learn the joint
relationship between the modalities. Instead, DIMNet learns a shared
representation for different modalities by mapping them individually to their
common covariates. These shared representations can then be used to find the
correspondences between the modalities. We show empirically that DIMNet is able
to achieve better performance than other current methods, with the additional
benefits of being conceptually simpler and less data-intensive.

提出了一种新的框架，称为 Disjoint Mapping Network (DIMNet)，用于跨模态生物识别匹配，特别是声音和面部。DIMNet 通过将它们分别映射到它们的共同协变量，学习了不同模态的共享表示，从而找到模态之间的对应关系，并且证明了 DIMNet 能够比其他现有方法具有更好的性能，而且在概念上更简单且不需要过多的数据。

语音与人脸跨模态匹配的不相交映射网络

Disjoint Mapping Network for Cross-modal Matching of Voices and Faces

State-of-the-art methods of attribute detection from faces almost always
assume the presence of a full, unoccluded face. Hence, their performance
degrades for partially visible and occluded faces. In this paper, we introduce
SPLITFACE, a deep convolutional neural network-based method that is explicitly
designed to perform attribute detection in partially occluded faces. Taking
several facial segments and the full face as input, the proposed method takes a
data driven approach to determine which attributes are localized in which
facial segments. The unique architecture of the network allows each attribute
to be predicted by multiple segments, which permits the implementation of
committee machine techniques for combining local and global decisions to boost
performance. With access to segment-based predictions, SPLITFACE can predict
well those attributes which are localized in the visible parts of the face,
without having to rely on the presence of the whole face. We use the CelebA and
LFWA facial attribute datasets for standard evaluations. We also modify both
datasets, to occlude the faces, so that we can evaluate the performance of
attribute detection algorithms on partial faces. Our evaluation shows that
SPLITFACE significantly outperforms other recent methods especially for partial
faces.

介绍了一种基于深度卷积神经网络的方法 SPLITFACE，针对部分遮挡的人脸设计了新的属性检测算法，利用分离式面部分段输入数据，并采用数据驱动方法来构建预测模型，以提高算法的性能，其在部分人脸较好地预测局部特征。与现有方法相比，SPLITFACE 在面部部分遮挡时表现更佳，取得了很好的检测效果。测试数据集包括 CelebA 和 LFWA 等。