Feature extraction and learning approaches for cancellable biometrics:A survey
作者机构：School of MathematicsPhysics and ComputingUniversity of Southern QueenslandToowoombaQueenslandAustralia School of ComputingEngineering and Mathematical SciencesLa Trobe UniversityMelbourneVictoriaAustralia School of Engineering and Information TechnologyUniversity of New South Wales at the Australian Defence Force Academy(UNSW@ADFA)CanberraAustralian Capital TerritoryAustralia
出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)
年 卷 期：2024年第9卷第1期
页 面：4-25页
核心收录：
学科分类：08[工学] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：Australian Research Council,Grant/Award Numbers:DP190103660,DP200103207,LP180100663 UniSQ Capacity Building Grants,Grant/Award Number:1008313
主 题：biometrics feature extraction
摘 要：Biometric recognition is a widely used technology for user *** the application of this technology,biometric security and recognition accuracy are two important issues that should be *** terms of biometric security,cancellable biometrics is an effective technique for protecting biometric *** recognition accuracy,feature representation plays a significant role in the performance and reliability of cancellable biometric *** to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community,especially from researchers of cancellable *** extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance,while the privacy of biometric data is *** survey informs the progress,trend and challenges of feature extraction and learning for cancellable biometrics,thus shedding light on the latest developments and future research of this area.
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