Tattoos have been used effectively as soft biometrics to assist law
enforcement in the identification of offenders and victims, as they contain
discriminative information, and are a useful indicator to locate members of a
criminal gang or organisation. Due to various privacy issues in the acquisition
of images containing tattoos, only a limited number of databases exists. This
lack of databases has delayed the development of new methods to effectively
retrieve a potential suspect's tattoo images from a candidate gallery. To
mitigate this issue, in our work, we use an unsupervised generative approach to
create a balanced database consisting of 28,550 semi-synthetic images with
tattooed subjects from 571 tattoo categories. Further, we introduce a novel
Tattoo Template Reconstruction Network (TattTRN), which learns to map the input
tattoo sample to its respective tattoo template to enhance the distinguishing
attributes of the final feature embedding. Experimental results with real data,
i.e., WebTattoo and BIVTatt databases, demonstrate the soundness of the
presented approach: an accuracy of up to 99% is achieved for checking at most
the first 20 entries of the candidate list.

我们使用无监督生成方法创建了一个平衡的数据库，并引入了一种新颖的纹身模板重建网络 (TattTRN)，该网络通过学习将输入的纹身样本映射到其相应的纹身模板，以增强最终特征嵌入的区别性属性。实验证明了此方法的可行性，对于最多检查候选列表的前 20 个条目，可以达到高达 99% 的准确率。

TattTRN：用于纹身检索的模板重构网络

TattTRN: Template Reconstruction Network for Tattoo Retrieval

The role of soft biometrics to enhance person recognition systems in
unconstrained scenarios has not been extensively studied. Here, we explore the
utility of the following modalities: gender, ethnicity, age, glasses, beard,
and moustache. We consider two assumptions: 1) manual estimation of soft
biometrics and 2) automatic estimation from two commercial off-the-shelf
systems (COTS). All experiments are reported using the labeled faces in the
wild (LFW) database. First, we study the discrimination capabilities of soft
biometrics standalone. Then, experiments are carried out fusing soft biometrics
with two state-of-the-art face recognition systems based on deep learning. We
observe that soft biometrics is a valuable complement to the face modality in
unconstrained scenarios, with relative improvements up to 40%/15% in the
verification performance when using manual/automatic soft biometrics
estimation. Results are reproducible as we make public our manual annotations
and COTS outputs of soft biometrics over LFW, as well as the face recognition
scores.

本研究探讨软生物特征在不受限场景下增强人脸识别系统的作用，并使用 LFW 数据集展示了软生物特征与深度学习人脸检测系统融合后能够相对提高 40％/15％的验证性能。

野外人脸软生物特征：最新研究，注释和 COTS 评估

Facial Soft Biometrics for Recognition in the Wild: Recent Works,  Annotation, and COTS Evaluation

Facial attributes, emerging soft biometrics, must be automatically and
reliably extracted from images in order to be usable in stand-alone systems.
While recent methods extract facial attributes using deep neural networks
(DNNs) trained on labeled facial attribute data, the robustness of deep
attribute representations has not been evaluated. In this paper, we examine the
representational stability of several approaches that recently advanced the
state of the art on the CelebA benchmark by generating adversarial examples
formed by adding small, non-random perturbations to inputs yielding altered
classifications. We show that our fast flipping attribute (FFA) technique
generates more adversarial examples than traditional algorithms, and that the
adversarial robustness of DNNs varies highly between facial attributes. We also
test the correlation of facial attributes and find that only for related
attributes do the formed adversarial perturbations change the classification of
others. Finally, we introduce the concept of natural adversarial samples, i.e.,
misclassified images where predictions can be corrected via small
perturbations. We demonstrate that natural adversarial samples commonly occur
and show that many of these images remain misclassified even with additional
training epochs, even though their correct classification may require only a
small adjustment to network parameters.

本文研究了利用深度神经网络提取面部特征的鲁棒性，通过生成对抗性样本测试了多种算法的可靠性，并提出了自然对抗样本的概念，发现即使在经过多次训练的情况下，网络仍然存在一些本该被正确分类的对抗样本。

面部属性：准确性和对抗鲁棒性

Facial Attributes: Accuracy and Adversarial Robustness

Person re-identification is critical in surveillance applications. Current
approaches rely on appearance based features extracted from a single or
multiple shots of the target and candidate matches. These approaches are at a
disadvantage when trying to distinguish between candidates dressed in similar
colors or when targets change their clothing. In this paper we propose a
dynamics-based feature to overcome this limitation. The main idea is to capture
soft biometrics from gait and motion patterns by gathering dense short
trajectories (tracklets) which are Fisher vector encoded. To illustrate the
merits of the proposed features we introduce three new "appearance-impaired"
datasets. Our experiments on the original and the appearance impaired datasets
demonstrate the benefits of incorporating dynamics-based information with
appearance-based information to re-identification algorithms.

本文提出了基于动力学特征的人物再识别方法，包括采集人体运动轨迹（tracklets）并进行 Fisher 向量编码等方法，通过实验证明了该方法在监控应用中的优越性。