Information is frequently retrieved from valid personal ID cards by the authorised organisation to address different purposes. The successful information retrieval (IR) depends on the accuracy and timing process. A process which necessitates a long time to respond is frustrating for both sides in the exchange of data. This paper aims to propose a series of state-of-the-art methods for the journey of an Identification card (ID) from the scanning or capture phase to the point before Optical character recognition (OCR). The key factors for this proposal are the accuracy and speed of the process during the journey. The experimental results of this research prove that utilising the methods based on deep learning, such as Efficient and Accurate Scene Text (EAST) detector and Deep Neural Network (DNN) for face detection, instead of traditional methods increase the efficiency considerably.
Vietnam National University, Ho Chi Minh City, Vietnam（越南国家大学，胡志明市，越南）；University of Information Technology, VNU-HCM, Ho Chi Minh City, Vietnam（信息技术大学，VNU-HCM，胡志明市，越南）
University of East Anglia（东安格利亚大学）；University of Pittsburgh（匹兹堡大学）；JD Artificial Intelligence Research（京东人工智能研究院）
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…
Robust retinal biometrics for patient identity verification and retrieval across age and imaging devices
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；Radboud University Medical Center（拉德堡德大学医学中心）；Institute of Molecular and Clinical Ophthalmology, University of Basel（巴塞尔大学分子与临床眼科学研究所）；University of Lausanne（洛桑大学）；Swiss Institute of Bioinformatics（瑞士生物信息学研究所）；University of Cape Town（开普敦大学）
We propose and investigate an identity sensitive joint embedding of face and voice. Such an embedding enables cross-modal retrieval from voice to face and from face to voice. We make the following four contributions: first, we show that the embedding can be learnt from videos of talking faces, without requiring any identity labels, using a form of cross-modal self-supervision; second, we develop a curriculum learning schedule for hard negative mining targeted to this task, that is essential for learning to proceed successfully; third, we demonstrate and evaluate cross-modal retrieval for ident…
Meitu Inc.（美图公司）；School of Information and Electronics,Beijing Institution of Technology（北京理工大学信息与电子学院）；Beijing Institution of Technology（北京理工大学）；School of Information and Electronics,Beijing Institution of Technology, China（北京理工大学信息与电子学院，中国）
This paper proposes an end-to-end deep hashing framework with category mask for fast video retrieval. We train our network in a supervised way by fully exploiting inter-class diversity and intra-class identity. Classification loss is optimized to maximize inter-class diversity, while intra-pair is introduced to learn representative intra-class identity. We investigate the binary bits distribution related to categories and find out that the effectiveness of binary bits is highly correlated with data categories, and some bits may degrade classification performance of some categories. We then des…
Identity Card Presentation Attack Detection: A Systematic Review
da/sec-Biometrics and Internet Security Research Group, Hochschule Darmstadt（da/sec生物识别与互联网安全研究组，达姆施塔特高等学院）；Universidad Autónoma de Manizales（自动化工学院）；Universidad de Caldas（卡利斯大学）
Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. Howe…
Face image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly. In order to conduct fast and accurate retrieval, a compact hash code-based methods have been proposed, and recently, deep face image hashing methods with supervised classification training have shown outstanding performance. However, classification-based scheme has a disadvantage in that it cannot reveal complex similarities between face images into the hash code learning. In this paper, we attempt to improve the fa…
da/sec-Biometrics and Internet Security Research Group, Hochschule Darmstadt（达姆施塔特应用技术大学生物识别与互联网安全研究组）；TOC Biometrics（TOC生物识别公司）
Removing the background in ID Card images is a real challenge for remote verification systems because many of the re-digitalised images present cluttered backgrounds, poor illumination conditions, distortion and occlusions. The background in ID Card images confuses the classifiers and the text extraction. Due to the lack of available images for research, this field represents an open problem in computer vision today. This work proposes a method for removing the background using semantic segmentation of ID Cards. In the end, images captured in the wild from the real operation, using a manually …
CityGuard: Graph-Aware Private Descriptors for Bias-Resilient Identity Search Across Urban Cameras
University of Macau（澳门大学）；Tsinghua University（清华大学）；Renmin University of China（中国人民大学）；Shanghai AI Laboratory（上海人工智能实验室）；The Hong Kong University of Science and Technology（香港科技大学）；Juntendo University（顺天堂大学）；Peking University（北京大学）
An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）；Frontis；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artifcial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research, Wuhan, China（武汉人工智能研究所，武汉，中国）；School of Computer Science, Wuhan University（武汉大学计算机学院）
