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Applied Sciences
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Application of Artificial Intelligence in Face Recognition Research
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Application of Artificial Intelligence in Face Recognition Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 2341

Special Issue Editor

Dr. Hirokazu Doi

E-Mail Website
Guest Editor
Department of Information and Management Systems Engineering, Nagaoka University of Technology, Niigata 940-2188, Japan
Interests: social perception; emotion; attractiveness computing; EEG/ERP; digital phenotyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The human face conveys diverse information encompassing emotions, the focus of attention, gender, age, and identity. The computational and neural basis of the human perceptual system’s ability to decode this information from a human face have generated much interest. Recently, cognitive neuroscientists have gained novel insights into the mechanism of face perception by applying machine learning to the analysis of behavioral, electrophysiological, and neuroimaging data. Accessible applications for automatic face recognition have empowered engineers to develop innovative man–machine interface and efficient analytical tools to quantify the expressiveness of facial information. This Special Issue, titled "Application of Artificial Intelligence in Face Recognition Research," aims to compile cutting-edge face recognition research assisted by artificial intelligence, showcasing how AI is transforming the landscape of face recognition research across various disciplines. We welcome all types of articles, including preliminary technical reports, research articles, and review articles. Potential research topics for this Special Issue include, but are not limited to, the following.

  • ML (machine learning)-based analysis of behavior and neural activation relevant to face recognition;
  • Novel application, system, or user-interface using automatic face recognition;
  • Proposition of new algorithm for face recognition;
  • Comparisons of biological and artificial systems for face recognition.

Dr. Hirokazu Doi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • face
  • machine learning
  • image processing
  • neural system

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Published Papers (2 papers)

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Research

13 pages, 1987 KiB  
Article
Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition
by Hansoo Kim
Appl. Sci. 2025, 15(7), 3613; https://doi.org/10.3390/app15073613 - 26 Mar 2025
Viewed by 339
Abstract
Advancements in science, technology, and computer engineering have significantly influenced biometric identification systems, particularly facial recognition. However, these systems are increasingly vulnerable to sophisticated forgery techniques. This study presents a novel deep learning framework optimized for texture analysis to detect facial forgeries effectively. [...] Read more.
Advancements in science, technology, and computer engineering have significantly influenced biometric identification systems, particularly facial recognition. However, these systems are increasingly vulnerable to sophisticated forgery techniques. This study presents a novel deep learning framework optimized for texture analysis to detect facial forgeries effectively. The proposed method leverages high-frequency texture features, such as roughness, color variation, and randomness, which are more challenging to replicate than specific facial features. The network employs a shallow architecture with wide feature maps to enhance efficiency and precision. Furthermore, a binary classification approach combined with supervised contrastive learning addresses data imbalance and strengthens generalization capabilities. Experimental results, conducted on three benchmark datasets (CASIA-FASD, CelebA-Spoof, and NIA-ILD), demonstrate the model’s robustness, achieving an Average Classification Error Rate (ACER) of approximately 0.06, significantly outperforming existing methods. This approach ensures practical applicability for real-time biometric systems, providing a reliable and efficient solution for forgery detection. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Face Recognition Research)
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16 pages, 943 KiB  
Article
Intrinsic Motivational States Can Be Classified by Non-Contact Measurement of Autonomic Nervous System Activation and Facial Expressions
by Sae Kawasaki, Koichi Ashida, Vinh-Tiep Nguyen, Thanh Duc Ngo, Duy-Dinh Le, Hirokazu Doi and Norimichi Tsumura
Appl. Sci. 2024, 14(15), 6697; https://doi.org/10.3390/app14156697 - 31 Jul 2024
Viewed by 1394
Abstract
Motivation is a primary driver of goal-directed behavior. Therefore, the development of cost-effective and easily applicable systems to objectively quantify motivational states is needed. To achieve our goal, this study investigated the feasibility of classifying high- and low-motivation states by machine learning based [...] Read more.
Motivation is a primary driver of goal-directed behavior. Therefore, the development of cost-effective and easily applicable systems to objectively quantify motivational states is needed. To achieve our goal, this study investigated the feasibility of classifying high- and low-motivation states by machine learning based on a diversity of features obtained by non-contact measurement of physiological responses and facial expression analysis. A random forest classifier with feature selection yielded modest success in the classification of high- and low-motivation states. Further analysis linked high-motivation states to the indices of autonomic nervous system activation reflective of reduced sympathetic activation and stronger, more intense expressions of happiness. The performance of motivational state classification systems should be further improved by incorporating different varieties of non-contact measurements. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Face Recognition Research)
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