Biometrics and Pattern Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 382

Special Issue Editor


E-Mail Website
Guest Editor
Institute for Basic Science, Yonsei University, Seoul, Republic of Korea
Interests: machine learning; pattern recognition; deep neural networks; stacking-based deep neural networks

Special Issue Information

Dear Colleagues,

The global market size of biometric technology has experienced significant growth, and this trend is expected to continue expanding in the coming years. This is attributed to the increasing utilization of biometric characteristics, e.g., face, fingerprint, voice, etc., in consumer electronics and automotive sectors, fueling a strong demand for user authentication, identification, and security and surveillance solutions. Hence, pattern recognition, a rapidly evolving field, drives the advancements in machine learning and extends its influence to computer vision, including biometrics for signal/image/video processing and analysis.

Topics of interests include, but are not limited to, the following:

  • Machine learning (ML) and embedding learning using deep learning (including lightweight architecture), and enhanced ML algorithms for biometrics and pattern recognition.
  • Biometrics, all physiological and behavioral attributes, including multimodal biometrics, and other relevant object recognition tasks, particularly those in the realistic open-set deployment setting.
  • Real-world and future artificial intelligence (AI) and ML systems/applications/surveys, such as security and monitoring, forensics, continuous authentication, law enforcement, aviation security, healthcare, transportation, smart homes, etc.
  • Fairness, accountability, transparency, and ethical (FATE) principles for AI and ML systems, especially biometrics.

Technical Program Committee Member:

Name: Dr. Kian Ming Lim
Email: [email protected]
Affiliation: School of Computer Science at the University of Nottingham Ningbo China, Ningbo 315100, China
Research Interests: machine learning; deep learning; computer vision; pattern recognition

Dr. Cheng Yaw Low
Guest Editor

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Keywords

  • pattern recognition
  • biometrics recognition
  • computer vision
  • machine learning
  • signal/image/video processing and analysis

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