IET Biometrics
Publishing Collaboration
More info
IET logo
 Journal metrics
See full report
Acceptance rate16%
Submission to final decision144 days
Acceptance to publication27 days
CiteScore5.700
Journal Citation Indicator0.380
Impact Factor2.0

Submit your research today

IET Biometrics is an open access journal, and articles will be immediately available to read and reuse upon publication.

Read our author guidelines

 Journal profile

IET Biometrics publishes original research and review articles in any topic which can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies. 

 Editor spotlight

IET Biometrics has two Chief Editors, and maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

 Abstracting and Indexing

This journal's articles appear in a wide range of abstracting and indexing databases, and are covered by numerous other services that aid discovery and access. Find out more about where and how the content of this journal is available.

Latest Articles

More articles
Research Article

Emotion Recognition Based on Handwriting Using Generative Adversarial Networks and Deep Learning

The quality of people’s lives is closely related to their emotional state. Positive emotions can boost confidence and help overcome difficulties, while negative emotions can harm both physical and mental health. Research has shown that people’s handwriting is associated with their emotions. In this study, audio-visual media were used to induce emotions, and a dot-matrix digital pen was used to collect neutral text data written by participants in three emotional states: calm, happy, and sad. To address the challenge of limited samples, a novel conditional table generative adversarial network called conditional tabular-generative adversarial network (CTAB-GAN) was used to increase the number of task samples, and the recognition accuracy of task samples improved by 4.18%. The TabNet (a neural network designed for tabular data) with SimAM (a simple, parameter-free attention module) was employed and compared with the original TabNet and traditional machine learning models; the incorporation of the SimAm attention mechanism led to a 1.35% improvement in classification accuracy. Experimental results revealed significant differences between negative (sad) and nonnegative (calm and happy) emotions, with a recognition accuracy of 80.67%. Overall, this study demonstrated the feasibility of emotion recognition based on handwriting with the assistance of CTAB-GAN and SimAm-TabNet. It provides guidance for further research on emotion recognition or other handwriting-based applications.

Research Article

A Comparative Study of Cross-Device Finger Vein Recognition Using Classical and Deep Learning Approaches

Finger vein recognition is gaining popularity in the field of biometrics, yet the inter-operability of finger vein patterns has received limited attention. This study aims to fill this gap by introducing a cross-device finger vein dataset and evaluating the performance of finger vein recognition across devices using a classical method, a convolutional neural network, and our proposed patch-based convolutional auto-encoder (CAE). The findings emphasise the importance of standardisation of finger vein recognition, similar to that of fingerprints or irises, crucial for achieving inter-operability. Despite the inherent challenges of cross-device recognition, the proposed CAE architecture in this study demonstrates promising results in finger vein recognition, particularly in the context of cross-device comparisons.

Research Article

Learning Deep Embedding with Acoustic and Phoneme Features for Speaker Recognition in FM Broadcasting

Text-independent speaker verification (TI-SV) is a crucial task in speaker recognition, as it involves verifying an individual’s claimed identity from speech of arbitrary content without any human intervention. The target for TI-SV is to design a discriminative network to learn deep speaker embedding for speaker idiosyncrasy. In this paper, we propose a deep speaker embedding learning approach of a hybrid deep neural network (DNN) for TI-SV in FM broadcasting. Not only acoustic features are utilized, but also phoneme features are introduced as prior knowledge to collectively learn deep speaker embedding. The hybrid DNN consists of a convolutional neural network architecture for generating acoustic features and a multilayer perceptron architecture for extracting phoneme features sequentially, which represent significant pronunciation attributes. The extracted acoustic and phoneme features are concatenated to form deep embedding descriptors for speaker identity. The hybrid DNN demonstrates not only the complementarity between acoustic and phoneme features but also the temporality of phoneme features in a sequence. Our experiments show that the hybrid DNN outperforms existing methods and delivers a remarkable performance in FM broadcasting TI-SV.

Research Article

On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition

With the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging.

Research Article

Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues

The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.

Research Article

The Impact of Illumination on Finger Vascular Pattern Recognition

This paper studies the impact of illumination direction and bundle width on finger vascular pattern imaging and recognition performance. A qualitative theoretical model is presented to explain the projection of finger blood vessels on the skin. A series of experiments were conducted using a scanner of our design with illumination from the top, a single-direction side (left or right), and narrow or wide beams. A new dataset was collected for the experiments, containing 4,428 NIR images of finger vein patterns captured under well-controlled conditions to minimize position and rotation angle differences between different sessions. Top illumination performs well because of more homogenous, which enhances a larger number of visible veins. Narrower bundles of light do not affect which veins are visible, but they reduce the overexposure at finger boundaries and increase the quality of vascular pattern images. The narrow beam achieves the best performance with 0% of [email protected]%, and the wide beam consistently results in a higher false nonmatch rate. The comparison of left- and right-side illumination has the highest error rates because only the veins in the middle of the finger are visible in both images. Different directional illumination may be interoperable since they produce the same vascular pattern and principally are the projected shadows on the finger surface. Score and image fusion for right- and left-side result in recognition performance similar to that obtained with top illumination, indicating the vein patterns are independent of illumination direction. All results of these experiments support the proposed model.

IET Biometrics
Publishing Collaboration
More info
IET logo
 Journal metrics
See full report
Acceptance rate16%
Submission to final decision144 days
Acceptance to publication27 days
CiteScore5.700
Journal Citation Indicator0.380
Impact Factor2.0
 Submit Evaluate your manuscript with the free Manuscript Language Checker

We have begun to integrate the 200+ Hindawi journals into Wiley’s journal portfolio. You can find out more about how this benefits our journal communities on our FAQ.