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Electronics
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Face Recognition Using Machine Learning
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Face Recognition Using Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 14950

Special Issue Editors

Dr. Tarek Gaber

E-Mail Website
Guest Editor
School of Science, Engineering & Environment, University of Salford, Greater Manchester M5 4WT, UK
Interests: biometric authentication and identification; cybersecurity; machine learning; secure software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Rania Hodhod

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: intelligent systems; computationnel intelligence; machine learning; serious games; computer science education
Special Issues, Collections and Topics in MDPI journals
Dr. Anastasia Angelopoulou

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: modeling and simulation; serious games; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Meselhy Eltoukhy

E-Mail Website
Guest Editor
Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
Interests: image processing; feature extraction and selelction; artifical intelligence; machine learning

Special Issue Information

Dear Colleagues,

Biometric systems aim to measure and analyse the unique physical or behavioral traits or characteristics of an individual. The main idea of a biometric system is the use of human structures reflecting distinctive characteristics. The human face is one of the main biometric traits which has found many real-world applications including military, finance, public security, law enforcement, health, education, marketing, entertainment, and human–computer interaction. The human face conveys information about a human’s identity, gender, age, race and emotions. The analysis of the human face data is not solely computer science research but is also an interdisciplinary field of research that involves psychology, neuroscience, and engineering. One of the main challenges of the face recognition problem in the adoption environments is that the information involved is usually complex and variable in practice. This is because of head pose, age, illumination, appearance change (due to make-up, using accessories (e.g., glasses, scarves), or facial hair), as well as similarity among individuals (e.g., relatives, twins).

The purpose of this Special Issue is to gather papers that define the advancement of face recognition using machine learning and their services and challenges. We are seeking the latest original contributions that have not been published elsewhere and are not currently under review by any other journal or conference. The potential topics of interest include, but are not limited to, the following:

  • Face recognition using machine learning;
  • Face recognition using deep learning;
  • Face recognition for access control systems;
  • Face recognition for biometric authentication;
  • Privacy preserving face recognition systems/methods;
  • Face recognition in education;
  • Face recognition in healthcare education;
  • Detection of face recognition attacks;
  • Face recognition in military;
  • Face recognition in marketing;
  • Bio-inspired optimization for face recognition;
  • Security issues in face recognition systems/methods;
  • Masked face recognition

Dr. Tarek Gaber
Prof. Dr. Rania Hodhod
Dr. Anastasia Angelopoulou
Dr. Mohamed Meselhy Eltoukhy
Guest Editors

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. Electronics 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.

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Published Papers (3 papers)

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15 pages, 2842 KiB  
Article
Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning
by Tamilarasan Ananth Kumar, Rajendrane Rajmohan, Muthu Pavithra, Sunday Adeola Ajagbe, Rania Hodhod and Tarek Gaber
Electronics 2022, 11(6), 904; https://doi.org/10.3390/electronics11060904 - 15 Mar 2022
Cited by 51 | Viewed by 7224
Abstract
The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments [...] Read more.
The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources. Full article
(This article belongs to the Special Issue Face Recognition Using Machine Learning)
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19 pages, 4616 KiB  
Article
Analysis of Random Local Descriptors in Face Recognition
by Airam Curtidor, Tetyana Baydyk and Ernst Kussul
Electronics 2021, 10(11), 1358; https://doi.org/10.3390/electronics10111358 - 7 Jun 2021
Cited by 10 | Viewed by 3453
Abstract
This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection [...] Read more.
This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition. Full article
(This article belongs to the Special Issue Face Recognition Using Machine Learning)
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18 pages, 6577 KiB  
Article
Novel BSSSO-Based Deep Convolutional Neural Network for Face Recognition with Multiple Disturbing Environments
by Neha Soni, Enakshi Khular Sharma and Amita Kapoor
Electronics 2021, 10(5), 626; https://doi.org/10.3390/electronics10050626 - 8 Mar 2021
Cited by 4 | Viewed by 2415
Abstract
Face recognition technology is presenting exciting opportunities, but its performance gets degraded because of several factors, like pose variation, partial occlusion, expression, illumination, biased data, etc. This paper proposes a novel bird search-based shuffled shepherd optimization algorithm (BSSSO), a meta-heuristic technique motivated by [...] Read more.
Face recognition technology is presenting exciting opportunities, but its performance gets degraded because of several factors, like pose variation, partial occlusion, expression, illumination, biased data, etc. This paper proposes a novel bird search-based shuffled shepherd optimization algorithm (BSSSO), a meta-heuristic technique motivated by the intuition of animals and the social behavior of birds, for improving the performance of face recognition. The main intention behind the research is to establish an optimization-driven deep learning approach for recognizing face images with multiple disturbing environments. The developed model undergoes three main steps, namely, (a) Noise Removal, (b) Feature Extraction, and (c) Recognition. For the removal of noise, a type II fuzzy system and cuckoo search optimization algorithm (T2FCS) is used. The feature extraction is carried out using the CNN, and landmark enabled 3D morphable model (L3DMM) is utilized to efficiently fit a 3D face from a single uncontrolled image. The obtained features are subjected to Deep CNN for face recognition, wherein the training is performed using novel BSSSO. The experimental findings on standard datasets (LFW, UMB-DB, Extended Yale B database) prove the ability of the proposed model over the existing face recognition approaches. Full article
(This article belongs to the Special Issue Face Recognition Using Machine Learning)
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