IEEE Beijing Biometrics Council Chapter

北京时间2022年5月6日下午，由IEEE生物识别联合会北京分会举办，中国科学院自动化研究所智能感知与计算研究中心NiCE团队承办的“生物特征识别与安全”学术论坛第6期活动圆满落幕！此次报告由中国科学院自动化研究所彭勃副研究员主持，邀请斯洛文尼亚卢布尔雅那大学Vitomir Štruc副教授带来主题为“Generative models in biometrics”的学术分享，观众通过线上会议、直播平台两种方式参与了此次活动。 On May 6, 2022, the 6th Session of "Trustworthy Biometrics Webinar" was successfully concluded, which was hosted by the IEEE Beijing Section Biometrics Council Chapter and organized by NiCE team from the Center for Research on Intelligent Perception and Computing of the Institute of Automation, Chinese Academy of Sciences. This report was chaired by Dr. Bo Peng, associate researcher from the Institute of Automation, Chinese Academy of Sciences, invited Vitomir Štruc, associate professor at the university of Ljubljana, Slovenia, to share on the themes of "Generative models in biometrics". Viewers participated in this event on online conferences and live streaming platform. 随着生成建模领域的发展以及Gnerative Adversarial Networks(GAN)等强大的模型框架的出现，近期涌现了各种各样的新技术和创新性算法，以解决包括生物识别技术在内的各种计算机视觉问题。在这次报告中，Vitomir Štruc首先简要概述生成模型的最新进展以及其团队在生成模型上的研究情况，然后展示如何将StyleGAN2生成器使用在生物识别技术相关研究上，并深入介绍以下内容：（1）基于GAN inversion的面部图像编辑技术-MaskFaceGAN，该技术允许对照片真实感进行操作并明确地解决了在基于隐空间编辑技术中常见的属性纠缠问题；（2）使用其团队的双支StyleGAN2(DB-StyleGAN2)模型完成bimodal ocular图像生成（及标注）任务。最后，Vitomir Štruc阐述了生成模型现有的一些问题与挑战及其未来的发展方向。 With the developments in the field of generative modeling and with the appearance of powerful model architectures, such as Generative Adversarial Networks (GAN), a wide range of new techniques and inventive algorithms has emerged recently to solve diverse computer vision problems, including many problems in the area of biometrics. In this talk, Prof. Vitomir Štruc first provided a short overview of recent advances in generative modeling and described some of their research efforts that focus explicitly on generative models. Next, Prof. Vitomir Štruc presented their recent biometrics-related research utilizing the powerful StlyGAN2 generator and talk about: (i) face image editing with their GAN inversion based MaskFaceGAN technique that allows for photo realistic image manipulation and explicitly addresses the problem of attribute entanglement seen with many latent-space based editing solutions, and (ii) bimodal ocular image generation (and annotation) with their Dual-Branch StlyGAN2 (DB-StyleGAN) model.  Finally, Prof. Vitomir Štruc elaborated on some of the existing challenges with generative models and highlight future research directions. Vitomir Štruc等人希望通过添加属性标签的方式实现目标图像特定属性的变化，而不改变其他属性。现有的基于latent space的方法能够生成真实感较好的图像，但是通过GAN inversion在隐空间编辑隐向量的方法往往不能实现很好的细节控制和属性解耦。Vitomir Štruc团队设计了MaskFaceGAN，首先使用人脸解析器face parser根据给定属性确定需要调整和保持的面部区域，然后设计损失函数约束隐码优化，最后借助人脸解析得到的mask，通过混合重组的方式得到最终的人脸图像。此外，通过调整损失函数细节，还能够实现多属性控制及细节调整。MaskFaceGAN实现了很好的属性解耦性能，并且在不同数据集上具备较好的普适性。 Prof. Vitomir Štruc and his team hope to change the specific attribute of the target image while keeping other attributes unchanged by adding target attribute label. So far, techniques based on latent space have achieved photo realistic images, but still can not achieve fine control and attribute disentanglement. Prof. Vitomir Štruc et al. design MaskFaceGAN, first use a face parser to make sure which part of the face to change and which part to maintain, then design the loss function to constrain the latent code optimization, and finally blend the generated image and the origin image to get the final output using masks. In addtion, by fine control of the loss function, they can also achieve multi-attributes and intensity control. MaskFaceGAN is able to disentangle the attributes well and adapt to different datasets generally. 现代生物特征识别系统往往基于深度学习模型，需要大量带有标注的数据，目前比较常见的做法是使用人工合成的数据集。Vitomir Štruc团队设计了BiOcularGAN，来生成逼真的人眼图像及其分割掩码。BiOcularGAN主要包括两部分，第一部分是Dual-Branch StyleGAN2，利用网络中分支结构，生成同一人眼分别在VIS和NIR域下的两张图像，作者假设如果能够以dual-branch的方式训练模型，模型就能具备更好的捕捉图像语义结构的能力；第二部分是一个语义分割图生成器，受DatasetGAN启发，语义分割图生成器利用Feature Maps和Ensemble Classifiers预测每个像素的语义标签，生成语义分割图。相较DatasetGAN，受益于dual-branch的训练方式，该模型能够更好地捕捉语义信息，从而生成更加精准的语义分割图。 Modern biometric systems always base on deep learning models, which need a lot of annotated data. Right now usual methods are using artificially synthesized dataset. Vitomir Štruc and his team design BiOcularGAN to generate realistic eye image and its segmentation mask. BiOcularGAN has mainly two parts, one is called Dual-Branch StyleGAN2, by using the Dual-Branch structure, it can generate two images of the same eye in VIS domin and NIR domin respectively. The author assume that if the model can be trained in a dual-branch manner, the model can have a better ability to capture the semantic structure of the image. The second part is a semantic mask generator, inspired by DatasetGAN, the semantic mask generator use feature maps and ensemble classifiers to predict the semantic label of each pixel to generate the semantic segmantation mask. Compared with DatasetGAN, benefited from the dual-branch training method, the model can capture the semantic information better and generate more precise semantic segmentation mask. 最后，Vitomir Štruc教授简单介绍了其团队的项目和研究方向，包括生物特征识别、人脸隐私保护和对抗攻击等，希望感兴趣的同学和老师能在未来与Vitomir Štruc团队开展相关方向上的合作。 Finally, Prof. Vitomir Štruc simply introduced the recent projects and research directions of his laboratory, not only biometrics, but also privacy protection, adversarial defenses and so on, and hoped to cooperate with the audience who are interested in the relevant directions in the future. 本次论坛Vitomir Štruc教授围绕生物特征识别中的生成模型（Generative models in biometrics），具体针对MaskFaceGAN和BiOcularGAN两项工作进行了分享，并与参会老师和同学进行交流探讨。最后，非常感谢来自各研究机构和高校的老师和同学踊跃参与，活动圆满成功。希望大家能继续支持与关注我们后续的系列活动，期待大家加入微信群一起讨论。我们欢迎更多来自世界各地的人加入我们，在未来一起探索更值得信赖的生物识别技术。 In this Webniar, Prof. Vitomir Štruc shared generative models in biometrics, specifically on MaskFaceGAN and BiOcularGAN. Finally, thank you very much for the active participation of teachers and students from research institutions and universities, the activity concluded successfully. I hope you can continue to support and pay attention to our follow-up activities, and look forward to your joining the wechat group for discussion. We welcome more people from all over the world to join us and explore more reliable biometric technology in the future.          

北京时间2022年3月15日，由IEEE生物识别联合会北京分会举办，中国科学院自动化研究所智能感知与计算研究中心NiCE团队承办的“生物特征识别与安全”学术论坛第5期活动圆满落幕！此次报告由中国科学院自动化研究所赫然研究员主持，邀请美国密歇根州立大学Arun Ross教授带来主题为“Altered Biometric Data:The Good and the Bad”的学术分享，观众通过线上会议、直播平台两种方式参与了此次活动。 On March 15, 2022, the 5th Session of "Trustworthy Biometrics Webinar" was successfully concluded, which was hosted by the IEEE Beijing Section Biometrics Council Chapter and organized by NiCE team from the Center for Research on Intelligent Perception and Computing of the Institute of Automation, Chinese Academy of Sciences. This report was chaired by Prof. Ran He, prominent researcher from the Institute of Automation, Chinese Academy of Sciences, invited Arun Ross, professor in the Department of Computer Science and Engineering at Michigan State University, to share on the themes of "Altered Biometric Data:The Good and the Bad". Viewers participated in this event on online conferences and live streaming platform. 生物识别技术使用指纹、人脸、虹膜、声音和步态等身体和行为特征来识别个体，然而获取到的生物特征数据可能出于某些原因而被修改。虽然一些修改旨在提高生物识别系统的性能，但也存在许多恶意的修改行为，例如欺骗或混淆身份。此外，数据可能在经历一系列更改操作后生成一组与原始数据近似重复（Near-duplicate）的数据。在本次讲座中，我们讨论以下问题和方法：(a) 如何检测被修改的生物特征数据；(b) 如何确定近似重复生物特征数据之间的关系，并构建表示它们变换顺序的系统发育树（Phylogeny Tree）；(c) 怎样利用更改的生物特征数据实现增强隐私。 Biometrics refers to the use of physical and behavioral traits such as fingerprints, face, iris, voice and gait to recognize an individual. The biometric data (e.g., a face image) acquired from an individual may be modified for several reasons. While some modifications are intended to improve the performance of a biometric system (e.g., image enhancement), others may be intentionally adversarial (e.g., spoofing or obfuscating an identity). Furthermore, the data may be subjected to a sequence of alterations resulting in a set of near-duplicate data (e.g., applying a sequence of image filters to an input face image). In this talk, we will discuss methods for (a) detecting altered biometric data; (b) determining the relationship between near-duplicate biometric data and constructing a phylogeny tree denoting the sequence in which they were transformed; and (c) using altered biometric data to enhance privacy. PRNU提取是一种传统的相机溯源方法，通过提取图像中的非均匀响应噪声（Photo-Response Nonuniformity Noise, PRNU），取其高频部分与噪声模板匹配，判断图像与模板是否出自同一相机或传感器。 PRNU(photo-response nonuniformity noise) extraction is a traditional camera traceability method. By extracting the PRNU in the image and matching high-frequency part with the noise template, judge whether the image and the template come from the same camera or sensor. 然而PRNU会暴露相机或传感器型号，可能造成用户隐私泄漏。Ross团队提出一种Sensor De-identification的方法，通过调整DCT coefficients修改图像的PRNU噪声，在保证图像生物特征识别准确率的前提下，降低了传感器识别的准确率，实现了隐私保护的功能。 However, PRNU will expose camera or sensor models, which may cause user privacy leakage. Ross team proposed a Sensor De-identification method, which modifies the PRNU noise of the image by adjusting DCT coefficients, reduces the accuracy of sensor recognition while ensuring the accuracy of image biometric recognitionand, and realizes the function of privacy protection. 随着多媒体合成技术的发展，出现了将两个甚至多个不同人脸合成为一张人脸图像（Morphed face）的技术，合成人脸可能与数据库中多个人脸有着高度的相似性，对人脸识别任务提出挑战。 With the development of multimedia synthesis technology, the technology of combining two or more different human faces into a morphed face appears. The morphed face may have high similarity with multiple human faces in the database, which poses a challenge to the task of face recognition. Ross教授等人以检测身份证件中的合成人脸图像为背景，提出一种使用条件生成网络cGAN检测合成人脸的方法。将证件图像和参考图像输入条件生成网络，输出图像与参考图像进行生物特征比较，可以通过相似度判别证件图像是否为合成图像。此方法还能用于发掘组成合成图像的其他人脸信息。 Prof. Ross et al. proposed a method of detecting morphed face in ID documents using conditional generation networks. Inputing the document image and the reference image into cGAN, and comparing biological characteristics of the output image with the reference image, whether the document image is a morphed image can be judged by similarity measure. This method can also be used to discover other face information that makes up the morphed face. 将光度变换（例如亮度和对比度调整）应用于人脸图像，可以创建一组近似重复的图像（Near Duplicates）。这些近似的图像在视觉上可能无法辨认。在数字图像取证中，从一组近似重复的图像中识别原始图像，并推断它们之间的变换关系非常重要。 Photometric transformations, such as brightness and contrast adjustment, can be applied to a face image repeatedly creating a set of near-duplicate images. These near-duplicates may be visually indiscernible. Identifying the original image from a set of near-duplicates, and deducing the relationship between them, is important in the context of digital image forensics. 通常通过构建一个图像系统发育树（Phylogeny Tree, IPT）来推断一组近似重复图像间的关系。Ross教授等人利用三种不同的基函数族来模拟重复图像之间的成对关系，通过对函数参数的似然比进行评估，得到两两图像间不对称的变换指标，再利用深度优先搜索构建系统发育树。该方法在溯源和系统发育树重建上都具备优异的性能。 This is commonly done by generating an image phylogeny tree. Ross et al. utilize three different families of basis functions to model pairwise relationships between near-duplicate images, compute the likelihood ratio of the estimated parameters of the function above to obtain the asymmetric measure, finally use depth first search to construct IPT. This method works well in both the root identification and IPT reconstruction. 万能指纹（MasterPrints）指能够和大量指纹匹配的真实或合成的指纹。尤其对于许多设备上的小型传感器，往往不能捕获完整的指纹信息，这时万能指纹可以与较多局部指纹（Partial Fingerprints）进行匹配，攻击者可以利用它们发动针对特定主体的字典攻击，从而破坏指纹识别系统的安全性。 Masterprints are real or synthetic fingerprints that can match a large number of fingerprints in the database. Especially for the small sensors on many devices, they often can not capture the complete fingerprint information. At this time, the masterprints can be matched with a lot partial fingerprints, then attackers can use them to launch dictionary attacks against specific subjects, so as to break the security of fingerprint recognization system. DeepMasterPrints基于WGAN生成指纹图像，使用潜变量进化技术（Latent Variable Evolution）生成能够与数据库中大量指纹匹配的万能指纹。 DeepMasterPrints generates fingerprints based on WGAN, using LVE(Latent Variable Evolution) to generate masterprints that can match with a large amount of prints in the database. 利用生物特征和数字取证技术，人们可以通过人脸图像来得到年龄，性别和种族等软生物特征（Soft-biometric Attributes），这可能会导致隐私的泄漏。Ross等人提出了一种基于GAN的半对抗网络（Semi-Adversarial Networks, SAN），通过图像扰动方法将软生物特征隐私赋予人脸图像，称为PrivacyNet。该网络修改输入人脸图像，使其可由人脸匹配器用于匹配目的，但不能由属性分类器可靠使用。此外，PrivacyNet允许用户选择模糊输入人脸图像中的特定属性（例如，年龄和种族），同时允许提取其他类型的属性。 Using biometircs and digital forensics techniques, we can deduce soft-biometric attributes such as age, gender and race from a face image, which can lead to privacy leakage. Ross et al. develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology, using a GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet. The network modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender). 本次论坛Arun Ross教授围绕修改的生物特征数据（Altered Biometric Data）的优点与缺点，具体针对数据生成，图像取证和隐私保护的几项工作进行了分享。最后，非常感谢来自各研究机构和高校的老师和同学踊跃参与，活动圆满成功。 In this Webniar, Professor Arun Ross shared the good and the bad of altered biometric data, specifically on several works of altered and synthetic data, digital image forensics and privacy protection. Finally, thank you very much for the active participation of teachers and students from research institutions and universities, the activity concluded successfully. 下一期“生物特征识别与安全”学术论坛我们邀请了IEEE Biometric Council的VP（vice president），来自University of Ljubljana, Slovenia的Vitomir Struc教授带来学术分享，Vitomir Struc教授目前在University of Ljubljana, Slovenia电气工程系担任副教授，Vitomir的研究围绕人工智能、模式识别、深度学习、生物特征识别、人脸识别、情感识别、信号处理、机器学习、计算机视觉和其他相关领域。希望大家能继续支持与关注我们后续的系列活动，期待大家加入微信群一起讨论。我们欢迎更多来自世界各地的人加入我们，在未来一起探索更值得信赖的生物识别技术。 Next session of "Trustworthy Biometrics Webinar", we invited the vice president of IEEE Biometric Council, Prof. Vitomir Struc to bring an academic sharing. Vitomir Struc is currently working as an Associate Professor at the Faculty of Electrical Engineering at the University of Ljubljana Slovenia, Vitomir’s research centers around artificial intelligence, pattern recognition, deep learning, biometrics, face recognition, emotion recognition, signal processing, machine learning, computer vision and other related areas. I hope you can continue to support and pay attention to our follow-up activities, and look forward to your joining the wechat group for discussion. We welcome more people from all over the world to join us and explore more reliable biometric technology in the future.

Abstract: The presentation will describe how the concept of electromagnetically soft and hard surfaces and metamaterial horns (metahorns) came about. I will also discuss practical antennas enabled by these EM techniques, as well as future opportunities and challenges in antenna and RF design. Co-sponsored by: Staracom Speaker(s): Dr. Erik Lier, Bldg: Pavillons Lassonde, M- , 2500 Chemin de Polytechnique, Montreal, Quebec, Canada, H3T1J4, Virtual: https://events.vtools.ieee.org/m/280076