Deep learning system have drawback that their output is not accompanied with ex-planation. In a domain such as forensic handwriting verification it is essential to provideexplanation to jurors. The goal of handwriting verification is to find a measure of confi-dence whether the given handwritten samples are written by the same or different writer.We propose a method to generate explanations for the confidence provided by convolu-tional neural network (CNN) which maps the input image to 15 annotations (features)provided by experts. Our system comprises of: (1) Feature learning network (FLN),a differentiable system, (2) Inference module for providing explanations. Furthermore,inference module provides two types of explanations: (a) Based on cosine similaritybetween categorical probabilities of each feature, (b) Based on Log-Likelihood Ratio(LLR) using directed probabilistic graphical model. We perform experiments using acombination of feature learning network (FLN) and each inference module. We evaluateour system using XAI-AND dataset, containing 13700 handwritten samples and 15 cor-responding expert examined features for each sample. The dataset is released for publicuse and the methods can be extended to provide explanations on other verification taskslike face verification and bio-medical comparison. This dataset can serve as the basis and benchmark for future research in explanation based handwriting verification. The code is available on github.
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for Handwriting Verification
South China University of Technology（华南理工大学）；Huazhong University of Science and Technology（华中科技大学）
尽管在线手写识别技术近年来取得了很大的进展，但由于数据集规模小，生物识别介质有限，其识别性能与实际应用相比仍有很大差距。因此，本文提出了一个新的手写验证基准数据集--多模态签名和数字串数据集（Multimodal Signature and Digit String，MSDS），该数据集由402个用户提供的两个子集组成：中文签名（MSDS-ChS）和令牌数字串（MSDS-TDS），每个子集包含20个真实样本和20个熟练的专家。MSDS-ChS由手写的中文签名组成，据我们所知，这是最大的公开可用的手写验证中文签名数据集，至少是现有在线数据集的八倍。同时，MSDS-TDS由手写的令牌数字串组成，即用户的实际电话号码，尚未被探索。分别对MSDS-ChS和MSDS-TDS进行了不同基线的广泛实验。令人惊讶的是，最先进的方法在MSDS-TDS上的验证性能通常优于MSDS-ChS，这表明手写令牌数字串可能是一种比手写中文签名更有效的生物特征。这是一个很有希望的发现，可以激励我们探索新的生物特征。MSDS数据集可在https://github.com/HCIILAB/MSDS上获得。
Privacy-Preserving Biometric Verification with Handwritten Random Digit String
School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息工程学院）；SCUT-Zhuhai Institute of Modern Industrial Innovation（华南理工大学珠海现代产业创新研究院）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；EI Product Department, Huawei Cloud Computing Technologies Co., Ltd.（华为云计算技术有限公司EI产品部）
University of Fribourg（弗里堡大学）；Smith College（史密斯学院）；University of Applied Sciences and Arts Northwestern Switzerland（瑞士西北应用科学与艺术大学）；University of Applied Sciences and Arts Western Switzerland（瑞士西部应用科学与艺术大学）
Capturing More: Learning Multi-Domain Representations for Robust Online Handwriting Verification
South China University of Technology（华南理工大学）；INTSIG Information Co. Ltd（INTSIG信息有限公司）；INTSIG-SCUT Joint Lab on Document Analysis and Recognition（INTSIG-SCUT文档分析与识别联合实验室）；SCUT-Zhuhai Institute of Modern Industrial Innovation（华南理工大学珠海现代工业创新研究院）
ClimateViz: A Benchmark for Statistical Reasoning and Fact Verification on Scientific Charts
University of Oxford（牛津大学）；Qilu University of Technology(Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））；Hong Kong University of Science and Technology（香港科学大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科学大学（广州））
Found in Translation: semantic approaches for enhancing AI interpretability in face verification
Université de Mons (UMONS)（蒙斯大学）；Université Libre de Bruxelles (ULB)（布鲁塞尔自由大学）；Laboratoire de Recherche de l'EPITA -- LRE（EPITA研究所（LRE））；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；CNRS（法国国家科学研究中心）；LIGM（信息与建模实验室）
