Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.2
3.9
Journals
Biomimetics
Special Issues
Exploration of Bioinspired Computer Vision and Pattern Recognition: 2nd Edition
share announcement

Exploration of Bioinspired Computer Vision and Pattern Recognition: 2nd Edition

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1564

Special Issue Editor

Dr. Qian Jiang

E-Mail Website
Guest Editor
Department of Computer Science, Yunnan University, Kunming, China
Interests: information and communication technology; image fusion; color image; computer vision; deep learning; image colorization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial neural networks, such as multilayer perceptual machines, feedback neural networks, convolutional neural networks, and spiking neural networks, are inspired by biological nervous systems and partially simulate them. The rapid development of deep learning offers new solutions to many problems and generates significant challenges. In the field of computer vision and pattern recognition, especially, AI has led to unprecedented disruptive changes. The challenge lies in further exploring the performance of AI, broadening the scope of AI applications, and driving technological development through advances in the field. Current applications, such as generative AI, have raised concerns about authenticity, and ethical concerns around AI applications have emerged, involving deepfakes, adversarial attacks, and the traceability of digital media. Therefore, there are still many aspects of artificial neural networks that need to be developed and many issues that need to be addressed. First, the application of AI in image processing is expanding, showing great potential in areas such as image enhancement and virtual reality, but there are still many areas where there are unresolved issues. Second, the authenticity of images poses a serious challenge, and traditional detection algorithms are often overwhelmed when working with these highly synthesized images. Finally, research on adversarial attacks brings new perspectives to the study of deep neural networks. Therefore, it is crucial to conduct further research on computer vision and pattern recognition.

In this context, this Special Issue seeks contributions on new advances in the fields of computer vision and pattern recognition to improve the application and development of neural networks; related research on neural networks’ application is also welcome.

Dr. Qian Jiang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial neural networks
  • bioinformatics
  • deepfake detection
  • image fusion
  • image colorization
  • image super-resolution
  • adversarial attack and defence on neural networks
  • pattern recognition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 3006 KB  
Article
Emotion Recognition from Facial Expressions Considering Individual Differences in Emotional Intelligence
by Yubin Kim, Ayoung Cho, Hyunwoo Lee and Mincheol Whang
Biomimetics 2026, 11(3), 174; https://doi.org/10.3390/biomimetics11030174 - 2 Mar 2026
Viewed by 610
Abstract
Facial expression recognition (FER) in naturalistic settings is constrained by label ambiguity and variability in stimulus–response alignment. Adopting a data-centric perspective, this study examined whether emotional intelligence (EI)-stratified training data influence FER performance by treating EI as a qualitative factor associated with affective [...] Read more.
Facial expression recognition (FER) in naturalistic settings is constrained by label ambiguity and variability in stimulus–response alignment. Adopting a data-centric perspective, this study examined whether emotional intelligence (EI)-stratified training data influence FER performance by treating EI as a qualitative factor associated with affective data consistency. Naturally elicited facial expressions were collected in a controlled emotion induction experiment with subjective arousal and valence ratings. Using response-driven labeling, neutral ratings were retained as indicators of ambiguity. Participants were grouped into High and Low EI based on the alignment between subjective evaluations and outputs from a pretrained affect estimator. Identical binary classifiers for arousal and valence recognition were trained while varying only the training data composition and evaluated across baseline, unambiguous, and ambiguous test sets using independent training repetitions with repetition-level statistical aggregation. EI-stratified training was associated with statistically detectable, context-dependent performance differences: group effects were observed primarily under baseline conditions and, to a lesser extent, under ambiguous conditions, whereas no reliable differences emerged under unambiguous conditions. Pooled discrimination differences were modest, but item-level analyses identified significant differences in classification correctness in specific task–condition combinations. Comparable patterns were observed across alternative backbone architectures. These findings indicate that FER performance in naturalistic contexts is influenced not only by model architecture but also by the statistical structure and internal coherence of the training data, supporting EI-informed data selection in ambiguity-prone scenarios. Full article
(This article belongs to the Special Issue Exploration of Bioinspired Computer Vision and Pattern Recognition: 2nd Edition)
►▼ Show Figures

Figure 1

17 pages, 304 KB  
Article
Bioinspired Deep Neural Networks for Predicting Income-Reporting Discontinuities in the Chilean Student Loan Program
by Yoslandy Lazo, Álex Paz, Broderick Crawford, Carlos Valle, Eduardo Rodriguez-Tello, Ricardo Soto, José Barrera-Garcia, Felipe Cisternas-Caneo and Benjamín López Cortés
Biomimetics 2026, 11(2), 98; https://doi.org/10.3390/biomimetics11020098 - 1 Feb 2026
Viewed by 648
Abstract
This study addresses discontinuity prediction in income reporting within the Chilean student loan program, a critical event for credit risk management. Although the literature has incorporated machine learning models to anticipate non-compliance behavior, a gap remains in the development of methodologically robust evaluations [...] Read more.
This study addresses discontinuity prediction in income reporting within the Chilean student loan program, a critical event for credit risk management. Although the literature has incorporated machine learning models to anticipate non-compliance behavior, a gap remains in the development of methodologically robust evaluations that integrate nonlinear imputation, imbalance correction, and repeated validation across multiple partitions. To address this need, a complete pipeline was implemented on a dataset of 22,303 records, including MissForest imputation, SMOTE-based balancing, and a comparative assessment of a biologically inspired Deep Neural Network (DNN) and a Random Forest (RF) classifier used as a classical baseline model, evaluated across 35 stratified partitions. The results show that the bioinspired DNN, as the primary focus of this study, consistently outperforms the RF in metrics such as AUC (0.9991 vs 0.9709), F1-score (0.9966 vs 0.9497), and agreement measures, while also exhibiting lower variability across partitions. The interpretability analysis indicates that financial variables account for the greatest influence on predictions, whereas demographic variables contribute minimally. The study provides a replicable and robust methodology aligned with risk analysis practices in student credit contexts. Full article
(This article belongs to the Special Issue Exploration of Bioinspired Computer Vision and Pattern Recognition: 2nd Edition)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop