ArticleJournal of healthcare engineering2021
A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 12 papers.
What it found
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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.
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.
Who cites it
12 citing papers in PubMed.
- RETRACTED: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.PloS one · 2025Article
- Machine learning based model for detecting depression during Covid-19 crisis.Scientific African · 2023Article
- Retracted: A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism.Journal of healthcare engineering · 2023Article
- Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder.Computational intelligence and neuroscience · 2022Article
- Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine.BioMed research international · 2022Article
- Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.BioMed research international · 2022Article
- PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer.BioMed research international · 2022Article
- Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.BioMed research international · 2022Article
- Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System.Computational intelligence and neuroscience · 2022Article
- Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm.Computational intelligence and neuroscience · 2022Article
- Aerobics Image Classification Algorithm Based on Modal Symmetry Algorithm.Computational intelligence and neuroscience · 2021Article
- A Robust Image Encrypted Watermarking Technique for Neurodegenerative Disorder Diagnosis and Its Applications.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
- Retraction · 2023-10-04Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/AC49866D10FE6517AD9CD0F17ACBB2
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.
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Registered trials
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.