Recently, cloud storage and processing have been widely adopted. Mobile users
in one family or one team may automatically backup their photos to the same
shared cloud storage space. The powerful face detector trained and provided by
a 3rd party may be used to retrieve the photo collection which contains a
specific group of persons from the cloud storage server. However, the privacy
of the mobile users may be leaked to the cloud server providers. In the
meanwhile, the copyright of the face detector should be protected. Thus, in
this paper, we propose a protocol of privacy preserving face retrieval in the
cloud for mobile users, which protects the user photos and the face detector
simultaneously. The cloud server only provides the resources of storage and
computing and can not learn anything of the user photos and the face detector.
We test our protocol inside several families and classes. The experimental
results reveal that our protocol can successfully retrieve the proper photos
from the cloud server and protect the user photos and the face detector.

本文提出一种云端隐私保护人脸检索方案，保护移动用户的照片和人脸检测器，并成功测试了该方案。

移动用户云端隐私保护人脸检索

Privacy Preserving Face Retrieval in the Cloud for Mobile Users

Face retrieval has received much attention over the past few decades, and
many efforts have been made in retrieving face images against pose,
illumination, and expression variations. However, the conventional works fail
to meet the requirements of a potential and novel task --- retrieving a
person's face image at a specific age, especially when the specific 'age' is
not given as a numeral, i.e. 'retrieving someone's image at the similar age
period shown by another person's image'. To tackle this problem, we propose a
dual reference face retrieval framework in this paper, where the system takes
two inputs: an identity reference image which indicates the target identity and
an age reference image which reflects the target age. In our framework, the raw
images are first projected on a joint manifold, which preserves both the age
and identity locality. Then two similarity metrics of age and identity are
exploited and optimized by utilizing our proposed quartet-based model. The
experiments show promising results, outperforming hierarchical methods.

该研究提出了一种双重参考人脸检索框架，通过利用四元数模型优化年龄相似度和身份相似度两个相似度度量，实现在给定特定年龄时检索特定人脸的目标。

双参考人脸检索

Dual-reference Face Retrieval

We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML)
method for large scale face retrieval. In contrast to previous works which were
limited to low dimensional features and small datasets, the proposed method
scales to large datasets with high dimensional face descriptors. It utilises
pairwise (dis-)similarity constraints as supervision and hence does not require
exhaustive class annotation for every training image. While, traditionally,
multi-task learning methods have been validated on same dataset but different
tasks, we work on the more challenging setting with heterogeneous datasets and
different tasks. We show empirical validation on multiple face image datasets
of different facial traits, e.g. identity, age and expression. We use classic
Local Binary Pattern (LBP) descriptors along with the recent Deep Convolutional
Neural Network (CNN) features. The experiments clearly demonstrate the
scalability and improved performance of the proposed method on the tasks of
identity and age based face image retrieval compared to competitive existing
methods, on the standard datasets and with the presence of a million distractor
face images.

本文提出了一种新的 CP-mtML 方法，可以应对高维人脸描述符和大规模数据集，通过利用成对的相似和非相似约束作为监督，不需要对每个训练图像进行详尽的类别注释，经过在不同任务和数据集上的实证验证，可以有效地提高人脸图像检索的性能。