IET Biometrics

Top Research Topics at IET Biometrics?

IET Biometrics generally zeroes in on subjects such as Artificial intelligence, Pattern recognition, Biometrics, Feature extraction and Facial recognition system. IET Biometrics addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Machine learning and Computer vision. IET Biometrics features works in Computer vision, more specifically Image resolution, and explores their relation to disciplines like Invariant (mathematics).

IET Biometrics focuses on Pattern recognition but sometimes tackles the closely related topic of Fingerprint which is concerned with Fingerprint recognition. Biometrics research in IET Biometrics involves the investigation of Identification (information) studies, all of which are linked to disciplines such as Matching (statistics). Topics in Feature extraction were tackled in line with various other fields like Feature (machine learning), Feature (computer vision), Image fusion, Image texture and Convolutional neural network.

While work presented in it provided substantial information on Convolutional neural network, it also covered topics in Artificial neural network and Deep learning. The study on Facial recognition system presented in IET Biometrics intersects with the topics under Spoofing attack. The studies on Contextual image classification discussed can also contribute to research in the domains of Classifier (UML) and Handwriting recognition.

• Artificial intelligence (70.38%)
• Pattern recognition (48.35%)
• Biometrics (44.30%)

What are the most cited papers published in the journal?

• Ear biometrics: a survey of detection, feature extraction and recognition methods (165 citations)
• Face spoofing detection from single images using texture and local shape analysis (144 citations)
• Overview of research on facial ageing using the FG-NET ageing database (143 citations)

Research areas of the most cited articles at IET Biometrics:

The journal papers are mainly concerned with subjects like Artificial intelligence, Biometrics, Pattern recognition, Feature extraction and Computer vision. Artificial intelligence study tackled in the published articles is connected to the field of Machine learning. The published papers explore research in Contextual image classification and overlapping concepts in Classifier (UML) to expand the discourse in Pattern recognition.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Computer vision
• Machine learning

The previous edition focused in particular on these issues:

The primary areas of discussion in IET Biometrics are Artificial intelligence, Biometrics, Computer vision, Pattern recognition and Facial recognition system. The journal facilitates discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning and Identification (information). Concepts in Keystroke logging, as well as related topics in Keystroke dynamics, are covered in the Biometrics research presented in IET Biometrics.

Many of the research works in Computer vision, specifically Segmentation and Video detection, closely connected to disciplines like Invariant (mathematics) and Radar. Aside from investigating topics in Sparse approximation under Pattern recognition, it also explores concepts in Locality. Feature extraction, Convolutional neural network and Human–computer interaction are some topics wherein Facial recognition system research discussed in it have an impact.

The most cited articles from the last journal are:

• Cross-ethnicity face anti-spoofing recognition challenge: A review (13 citations)
• Extended evaluation of the effect of real and simulated masks on face recognition performance. (8 citations)
• Face morphing detection in the presence of printing/scanning and heterogeneous image sources (5 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in IET Biometrics (based on the number of publications) are:

• Christoph Busch (23 papers) published 6 papers at the last edition, 4 more than at the previous edition,
• Luuk Spreeuwers (13 papers) published 3 papers at the last edition,
• Andreas Uhl (13 papers) published 3 papers at the last edition, 1 more than at the previous edition,
• Christian Rathgeb (12 papers) published 2 papers at the last edition the same number as at the previous edition,
• Raymond N.J. Veldhuis (11 papers) published 2 papers at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in IET Biometrics (based on the number of publications) are:

• Instituto Superior Técnico (8 papers) published 1 paper at the last edition the same number as at the previous edition,
• Darmstadt University of Applied Sciences (8 papers) published 5 papers at the last edition, 4 more than at the previous edition,
• University of Twente (6 papers) published 3 papers at the last edition,
• Chinese Academy of Sciences (6 papers) published 2 papers at the last edition,
• University of Salzburg (6 papers) published 3 papers at the last edition, 2 more than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 0.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 29.09% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.09% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.82% of all publications and 40.00% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Real-World Applications of IET Biometrics Research

One important aspect of the IET Biometrics journal that this article could delve deeper into is the real-world applications of the research being presented. Examining how the concepts of artificial intelligence, pattern recognition, feature extraction, and more have been utilized in practical scenarios would give readers a comprehensive understanding of the impact of these studies. For instance, biometrics research has been pivotal in education, and specifically in personalized learning. Complex pattern recognition and artificial intelligence algorithms are being integrated into educator's tools to understand student learning patterns, strengths, weaknesses, and optimal learning conditions, enhancing the educational experience. If you are interested in this field, having a fundamental knowledge in these areas is essential. For more information on the necessary qualifications and skill set, you can refer to our guide on how to become a middle school math teacher in colorado. Additionally, facial recognition systems equipped with real-time feature extraction capabilities have greatly improved security measures, providing reliable and efficient identification. These technological advancements have fundamentally transformed security operations across several sectors, from the corporate world to law enforcement agencies. Therefore, the IET Biometrics research goes beyond theoretical implications, bridging the gap between technology and tangible, impactful applications in our day-to-day lives.