Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

被引：19

作者：

Wang, Min ^{[1
]}

Yin, Xuefei ^{[1
]}

Zhu, Yanming ^{[2
]}

Hu, Jiankun ^{[1
]}

机构：

[1] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia

[2] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW 2052, Australia

来源：

SENSORS | 2022年 / 22卷 / 14期

基金：

澳大利亚研究理事会;

关键词：

biometrics; biological signal; classification; deep learning; feature extraction; pattern recognition; representation learning; CONVOLUTIONAL NEURAL-NETWORKS; JOINT FEATURE-EXTRACTION; PERSON IDENTIFICATION; EEG SIGNALS; FUNCTIONAL CONNECTIVITY; USER IDENTIFICATION; HEART-SOUND; ECG; CLASSIFICATION; SYSTEM;

D O I：

10.3390/s22145111

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.

引用

页数：34

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