In this paper, we present a novel architecture that integrates a deep hashing
framework with a neural network decoder (NND) for application to face template
protection. It improves upon existing face template protection techniques to
provide better matching performance with one-shot and multi-shot enrollment. A
key novelty of our proposed architecture is that the framework can also be used
with zero-shot enrollment. This implies that our architecture does not need to
be re-trained even if a new subject is to be enrolled into the system. The
proposed architecture consists of two major components: a deep hashing (DH)
component, which is used for robust mapping of face images to their
corresponding intermediate binary codes, and a NND component, which corrects
errors in the intermediate binary codes that are caused by differences in the
enrollment and probe biometrics due to factors such as variation in pose,
illumination, and other factors. The final binary code generated by the NND is
then cryptographically hashed and stored as a secure face template in the
database. The efficacy of our approach with zero-shot, one-shot, and multi-shot
enrollments is shown for CMU-PIE, Extended Yale B, WVU multimodal and Multi-PIE
face databases. With zero-shot enrollment, the system achieves approximately
85% genuine accept rates (GAR) at 0.01% false accept rate (FAR), and with
one-shot and multi-shot enrollments, it achieves approximately 99.95% GAR at
0.01% FAR, while providing a high level of template security.

本文提出一种结合深度哈希框架和神经网络解码器（NND）的新型体系结构，用于人脸模板保护，可以在零 - 拍摄、一次拍摄和多次拍摄注册的情况下提供更好的匹配性能，并且由于不需要重新训练，可以避免添加新主体时需要重新训练的问题.

基于零样本深度哈希和神经网络的人脸模板保护纠错

Zero-Shot Deep Hashing and Neural Network Based Error Correction for  Face Template Protection

Polar codes have drawn much attention and been adopted in 5G New Radio (NR)
due to their capacity-achieving performance. Recently, as the emerging deep
learning (DL) technique has breakthrough achievements in many fields, neural
network decoder was proposed to obtain faster convergence and better
performance than belief propagation (BP) decoding. However, neural networks are
memory-intensive and hinder the deployment of DL in communication systems. In
this work, a low-complexity recurrent neural network (RNN) polar decoder with
codebook-based weight quantization is proposed. Our test results show that we
can effectively reduce the memory overhead by 98% and alleviate computational
complexity with slight performance loss.

本文提出一种基于代码簿加权量化的低复杂度循环神经网络极化解码器，可以有效减少内存开销，在轻微性能损失的情况下减轻计算复杂度。

基于简化复杂度的循环神经网络极化码译码器及其权重量化机制

Low-complexity Recurrent Neural Network-based Polar Decoder with Weight  Quantization Mechanism

Most approaches for video frame interpolation require accurate dense
correspondences to synthesize an in-between frame. Therefore, they do not
perform well in challenging scenarios with e.g. lighting changes or motion
blur. Recent deep learning approaches that rely on kernels to represent motion
can only alleviate these problems to some extent. In those cases, methods that
use a per-pixel phase-based motion representation have been shown to work well.
However, they are only applicable for a limited amount of motion. We propose a
new approach, PhaseNet, that is designed to robustly handle challenging
scenarios while also coping with larger motion. Our approach consists of a
neural network decoder that directly estimates the phase decomposition of the
intermediate frame. We show that this is superior to the hand-crafted
heuristics previously used in phase-based methods and also compares favorably
to recent deep learning based approaches for video frame interpolation on
challenging datasets.

本文介绍了一种新的视频帧插值方法 ——PhaseNet。相较于传统方法和基于深度学习方法，PhaseNet 可以更好地处理光照变化和运动模糊等挑战性场景，并且也适用于更大范围的运动，通过直接估计中间帧的相位分解，避免了使用手工设计启发式方法的缺点。