Evidence map›Paper›PMID 35757492›Full record

ArticleJournal of ambient intelligence and humanized computing2022

MDFNet: an unsupervised lightweight network for ear print recognition.

Oussama Aiadi, Belal Khaldi, Cheraa Saadeddine

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of ambient intelligence and humanized computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Oussama AiadiLINATI Laboratory, Department of Computer Science and Information Technology, University of Kasdi Merbah, 30000 Ouargla, Algeria.ORCID https://orcid.org/0000-0002-4102-1735
Belal KhaldiLINATI Laboratory, Department of Computer Science and Information Technology, University of Kasdi Merbah, 30000 Ouargla, Algeria.
Cheraa SaadeddineLINATI Laboratory, Department of Computer Science and Information Technology, University of Kasdi Merbah, 30000 Ouargla, Algeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we propose an unsupervised lightweight network with a single layer for ear print recognition. We refer to this method by MDFNet because it relies on gradient Magnitude and Direction alongside with responses of data-driven Filters. At first, we align ear using Convolution Neural Network (CNN) and Principal Component Analysis (PCA). MDFNet starts by generating a filter bank from training images using PCA. This is followed by a twofold layer, which comprises two operations namely convolution using learned filters and computation of gradient image. To prevent over-fitting, a binary hashing process is done by combining different filter responses into a single feature map. Then, we separately construct histograms for each of gradient magnitude and direction according to the feature map. These histograms are then normalized, using power-L

Indexed as

BiometricsCNNDeep learningRecognitionUnconstrained earUnsupervised learning

Identifiers

PMID35757492
PMCPMC9206135

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.