Evidence map›Paper›PMID 35890799›Full record

ReviewSensors (Basel, Switzerland)2022

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.

Min Wang, Xuefei Yin, Yanming Zhu, Jiankun Hu

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Min WangSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia.ORCID 0000-0002-1580-6387
Xuefei YinSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia.ORCID 0000-0002-5784-7419
Yanming ZhuSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0002-8238-8090
Jiankun HuSchool of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia.ORCID 0000-0003-0230-1432

Funding

Australian Research Council DP200103207
6 · The paper itself

Abstract

Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.

Indexed as

BiometryCognitionbiological signalbiometricsclassificationdeep learningfeature extractionpattern recognitionrepresentation learning

Identifiers

PMID35890799
PMCPMC9320620

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.