Write in a clean editor, then format for IEEE Transactions on Biometrics, Behavior and Identity Science in one click — DocuGuru applies the official IEEE template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
IEEE Transactions on Biometrics, Behavior and Identity Science is a peer-reviewed journal published by IEEE, covering Face recognition and analysis, Biometric Identification and Security, Face and Expression Recognition. Over its lifetime it has published 539 papers, which have received 6,351 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Biometrics, Behavior and Identity Science, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Biometrics, Behavior and Identity Science style. No sign-up. |
| Publishes research in | Face recognition and analysis Biometric Identification and Security Face and Expression Recognition User Authentication and Security Systems Gait Recognition and Analysis |
| ISSN | 2637-6407 |
| Citation impact (2-yr) | 2.89 |
| h-index | 41 |
| i10-index | 159 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
During COVID-19 coronavirus epidemic, almost everyone wears a mask to prevent the spread of virus. It raises a problem that the traditional face recognition model basically fails in the scene of face-based identity verification, such as security check, community visit check-in, etc. Therefore, it is imminent to boost the performance of masked face recognition. Most…
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To…
The availability of large annotated datasets and affordable computation power have led to impressive improvements in the performance of convolutional neural networks (CNNs) on various face analysis tasks. In this paper, we describe a deep learning pipeline for unconstrained face identification and verification which achieves state-of-the-art performance on several benchmark datasets. We provide the design…
Model-based gait recognition is considered to be promising due to the robustness against some variations, such as clothing and baggage carried. Although model-based gait recognition has not been fully explored due to the difficulty of human body model fitting and the lack of a large-scale gait database, recent progress in deep learning-based approaches to human…
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top performing single and ensemble system submissions from…
IEEE Transactions on Energy Conversion
IEEE Transactions on Energy Markets, Policy, and Regulation
IEEE Transactions on Engineering Management
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Field Robotics
IEEE Transactions on Fuzzy Systems
ACM Transactions on Computational Logic
ACM Transactions on Computer Systems
