Evidence map›Paper›PMID 40740037›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2025

Multimodal biometric authentication system leveraging optimally trained ensemble classifier using feature-level fusion.

Khushboo Jha, Aruna Jain, Sumit Srivastava

Expression of concernAbstract read
PubMed Publisher
In one paragraph

Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It carries an expression of concern. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Khushboo JhaDepartment of Computer Science and Engineering, Birla Institute of Technology Mesra, Jharkhand, India.ORCID 0000-0003-1062-8128
Aruna JainDepartment of Computer Science and Engineering, Birla Institute of Technology Mesra, Jharkhand, India.
Sumit SrivastavaDepartment of Computer Science and Engineering, Birla Institute of Technology Mesra, Jharkhand, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ObjectiveThis study aims to enhance cybersecurity by implementing a robust biometric-based authentication approach. A Multimodal Biometric System (MBS) is proposed, utilizing feature-level fusion of human facial (physiological) and speech (behavioral) features to improve security, accuracy, and user convenience. The system addresses the limitations of traditional authentication methods, including unimodal biometrics and password-based security.BackgroundIn the modern digital landscape, human-computer interaction and digital platforms play a crucial role in daily life. With billions of users engaging in social media, financial transactions, and e-commerce, the demand for secure authentication mechanisms has intensified. However, the increasing sophistication of cyber threats poses significant risks, undermining trust, security, and confidence in digital systems.

Indexed as

Ensemble classifierface recognitionfeature level fusionimproved manta ray foraging optimizationmultimodal biometric systemsecurity and privacyspeaker recognition

Identifiers

PMID40740037

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.