Recently, finger knuckle prints (FKPs) have gained attention due to their
rich textural patterns, positioning them as a promising biometric for identity
recognition. Prior FKP recognition methods predominantly leverage first-order
feature descriptors, which capture intricate texture details but fail to
account for structural information. Emerging research, however, indicates that
second-order textures, which describe the curves and arcs of the textures,
encompass this overlooked structural information. This paper introduces a novel
FKP recognition approach, the Dual-Order Texture Competition Network (DOTCNet),
designed to capture texture information in FKP images comprehensively. DOTCNet
incorporates three dual-order texture competitive modules (DTCMs), each
targeting textures at different scales. Each DTCM employs a learnable texture
descriptor, specifically a learnable Gabor filter (LGF), to extract texture
features. By leveraging LGFs, the network extracts first and second order
textures to describe fine textures and structural features thoroughly.
Furthermore, an attention mechanism enhances relevant features in the
first-order features, thereby highlighting significant texture details. For
second-order features, a competitive mechanism emphasizes structural
information while reducing noise from higher-order features. Extensive
experimental results reveal that DOTCNet significantly outperforms several
standard algorithms on the publicly available PolyU-FKP dataset.

近年来，指节纹理 (FKP) 因其丰富的纹理图案而受到关注，使其成为一个有前景的用于身份识别的生物特征。先前的 FKP 识别方法主要利用一阶特征描述符，能捕捉纹理的细节，但无法考虑结构信息。然而，新兴的研究表明，描述纹理曲线和弧线的二阶纹理包含了被忽视的结构信息。本文介绍了一种新颖的 FKP 识别方法，Dual-Order Texture Competition Network (DOTCNet)，旨在全面地捕捉 FKP 图像中的纹理信息。DOTCNet 包括三个双阶纹理竞争模块 (DTCMs)，分别针对不同尺度的纹理。每个 DTCM 采用可学习的纹理描述符，具体来说就是可学习的 Gabor 滤波器 (LGF)，用于提取纹理特征。通过利用 LGF，网络提取一阶和二阶纹理来全面描述精细纹理和结构特征。此外，一种注意力机制增强了一阶纹理中的相关特征，从而突出了重要的纹理细节。对于二阶纹理特征，一种竞争机制强调了结构信息，同时减少了高阶特征的噪声。广泛的实验证明，DOTCNet 在公开可用的 PolyU-FKP 数据集上明显优于几种标准算法。

超越一阶：一个多尺度的指节纹理生物特征识别方法

Beyond First-Order: A Multi-Scale Approach to Finger Knuckle Print  Biometrics

Despite generative adversarial networks (GANs) can hallucinate
photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they
cannot guarantee preserving the identities of hallucinated HR faces, making the
HR faces poorly recognizable. To address this problem, we propose a Siamese GAN
(SiGAN) to reconstruct HR faces that visually resemble their corresponding
identities. On top of a Siamese network, the proposed SiGAN consists of a pair
of two identical generators and one discriminator. We incorporate
reconstruction error and identity label information in the loss function of
SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the
generator pair and discriminator of SiGAN, we cannot only achieve
photo-realistic face reconstruction, but also ensures the reconstructed
information is useful for identity recognition. Experimental results
demonstrate that SiGAN significantly outperforms existing face hallucination
GANs in objective face verification performance, while achieving
photo-realistic reconstruction. Moreover, for input LR faces from unknown
identities who are not included in training, SiGAN can still do a good job.

提出了一种基于 Siamese 网络的生成对抗网络（SiGAN）来重建视觉上类似于其对应身份的高分辨率人脸，包括重建误差和身份标签信息，并在 SiGAN 的生成器对和鉴别器中迭代优化，实现了照片般逼真的面部重建和身份识别。实验结果表明，SiGAN 在客观面部验证性能上明显优于现有的人脸幻觉 GAN，并实现了照片般逼真的重建。此外，对于未包含在训练中的未知身份输入 LR 面孔，SiGAN 仍然可以做得很好。

SiGAN: Siamese 对抗生成网络用于保留身份的人脸超分辨率

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving  Face Hallucination

We explore the task of recognizing peoples' identities in photo albums in an
unconstrained setting. To facilitate this, we introduce the new People In Photo
Albums (PIPA) dataset, consisting of over 60000 instances of 2000 individuals
collected from public Flickr photo albums. With only about half of the person
images containing a frontal face, the recognition task is very challenging due
to the large variations in pose, clothing, camera viewpoint, image resolution
and illumination. We propose the Pose Invariant PErson Recognition (PIPER)
method, which accumulates the cues of poselet-level person recognizers trained
by deep convolutional networks to discount for the pose variations, combined
with a face recognizer and a global recognizer. Experiments on three different
settings confirm that in our unconstrained setup PIPER significantly improves
on the performance of DeepFace, which is one of the best face recognizers as
measured on the LFW dataset.

本文介绍了 PPIPA 数据集，提出了一种基于深度卷积网络的 Pose Invariant PErson Recognition (PIPER) 方法来实现照片集中个体识别，实验证明该方法在处理姿态、服装、视角、分辨率和光照等变化时表现优于 DeepFace。