认知生物识别中的表示学习与模式识别：综述。
Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.
机构信息
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia.
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.
出版信息
Sensors (Basel). 2022 Jul 7;22(14):5111. doi: 10.3390/s22145111.
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.
认知生物识别是生物识别技术的一个新兴分支。最近的研究表明，认知生物识别在各种应用中具有很大的潜力，包括生物识别识别和认知和情绪状态识别。非常有必要总结该领域的最新发展。现有的调查主要集中在一小部分认知生物识别模式上，如 EEG 和 ECG。本文对认知生物识别进行了全面的综述，涵盖了所有主要的生物信号模式和应用。设计了一个分类法来构建相应的知识，并从信号采集和预处理到表示学习和模式识别指导调查。我们为这些四个方面的各种生物信号和应用中的方法学进步提供了统一的视图，促进了跨领域的跨学科研究和知识转移。此外，本文讨论了认知生物识别中的开放研究方向，并提出了开发可靠和安全的认知生物识别系统的未来展望。
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