Mobile Recognition of Image Components Based on Machine Learning Methods

Authors

  • Galyna Kondratenko Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine
  • Ievgen Sidenko Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine https://orcid.org/0000-0001-6496-2469
  • Maksym Saliutin Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine
  • Yuriy Kondratenko 1) Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine 2) Institute of Artificial Intelligence Problems, Mala Zhytomyrs’ka Str., 11/5, Kyiv, 01001, Ukraine

DOI:

https://doi.org/10.13052/jmm1550-4646.2038

Keywords:

mobile recognition, image components, mask, machine learning methods, neural networks

Abstract

This paper is related to the recognition of certain components in images using machine learning methods and mobile technologies. The main result of this work is a developed system for recognizing the presence of a mask on the face using an image, which provides all the necessary information in real-time about the presence or absence of a mask on the face. When the program is turned off, statistics about the presence/absence of the mask will be recorded in the database. To achieve the goal, the following tasks were solved: the current state of the task of recognizing the presence of a mask on a person’s face was analysed; existing analogs of the systems were analysed; the necessary neural network architecture was selected as one of the machine learning methods; developed a system for recognizing the presence of a mask on the face using the necessary libraries; a user graphical interface, a database model for recording statistics and additional functionality have been developed; conduct testing. Practical application has a fairly wide range, in particular, the developed intelligent system is intended for use in the subway, industrial enterprises, state institutions, educational institutions, offices, and other public places. The developed system recognizes and records statistics about the presence of a mask on a person’s face using neural networks.

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Author Biographies

Galyna Kondratenko, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

Galyna Kondratenko is an Associate Professor, Ph.D., Associate Professor of the Intelligent Information Systems Department, Senior Researcher at Petro Mohyla Black Sea National University, Ukraine. She is a specialist in control systems, decision-making, fuzzy logic. She worked in the framework of international scientific university cooperation during the implementation of international projects with the European Union: TEMPUS (Cabriolet), Erasmus + (Aliot) and DAAD-Ostpartnerschaftsprogramm (project with the University of Saarland, Germany). Her research interests include computer control systems, fuzzy logic, decision-making, intelligent robotic devices.

Ievgen Sidenko, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

Ievgen Sidenko is an Associate Professor, Ph.D., Associate Professor of the Intelligent Information Systems Department at Petro Mohyla Black Sea National University (PMBSNU), Ukraine. He has received master degree in speciality “Intelligent decision making systems” (2010) at PMBSNU and Ph.D. degree in “Information technologies” (2015) at PMBSNU. His research interests include fuzzy sets and fuzzy logic, decision-making, optimization methods, neural networks, data mining, clustering and classification.

Maksym Saliutin, Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine

Saliutin Maksym is a master’s student of the Intelligent Information Systems Department at Petro Mohyla Black Sea National University (PMBSNU). He received his bachelor's degree with a major in 122 "Computer Science" at PMBSNU. He is engaged in creation of automation systems, as well as in development of neural network models. He is interested in studying new technologies in the field of machine learning and neural networks.

Yuriy Kondratenko, 1) Petro Mohyla Black Sea National University, 68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine 2) Institute of Artificial Intelligence Problems, Mala Zhytomyrs’ka Str., 11/5, Kyiv, 01001, Ukraine

Yuriy Kondratenko is Doctor of Science, Professor, Honour Inventor of Ukraine (2008), Corr. Academician of Royal Academy of Doctors (Barcelona, Spain), Head of the Intelligent Information Systems Department at Petro Mohyla Black Sea National University (PMBSNU), Leading Researcher at the Institute of Artificial Intelligence Problems under MES and NAS of Ukraine. He has received (a) the Ph.D. (1983) and Dr.Sc. (1994) in Elements and Devices of Computer and Control Systems from Odessa National Polytechnic University, (b) several international grants and scholarships for conducting research at Institute of Automation of Chongqing University, P.R.China (1988-1989), Ruhr-University Bochum, Germany (2000, 2010), Nazareth College and Cleveland State University, USA (2003), (c) Fulbright Scholarship for researching in USA (2015/2016) at the Dept. of Electrical Engineering and Computer Science in Cleveland State University. Research interests include robotics, automation, sensors and control systems, intelligent decision support systems, fuzzy logic.

References

A. Sheremet, Y. Kondratenko, I. Sidenko, G. Kondratenko, ‘Diagnosis of Lung Disease Based on Medical Images Using Artificial Neural Networks’, 3rd Ukraine Conference on Electrical and Computer Engineering, Lviv, Ukraine, 2021.

D, Chumachenko, et al., ‘Forecasting of COVID-19 Epidemic Process in Ukraine and Neighboring Countries by Gradient Boosting Method’, in: E, Faure, et al. (eds) Information Technology for Education, Science, and Technics. Lecture Notes on Data Engineering and Communications Technologies, 178, Springer, Cham, 2023.

S. Thuseethan, et al., ‘Deep COVID-19 Recognition Using Chest X-ray Images: A Comparative Analysis’, International Conference on Artificial Intelligence, Colombo, Sri Lanka, 2021.

A. Basu, M. F. Ali, ‘COVID-19 Face Mask Recognition with Advanced Face Cut Algorithm for Human Safety Measures’, 12th International Conference on Computing Communication and Networking Technologies, Kharagpur, India, 2021.

J. Luo, R. Luo, ‘Research on Image Recognition based on Reinforcement Learning’, 4th International Conference on Computer Vision, Image and Deep Learning, Zhuhai, China, 2023.

Y. Xing, W. Cai, ‘Mask recognition system based on anchor’, 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference, Chongqing, China, 2022.

G. Deore, R. Bodhula, V. Udpikar, V. More, ‘Study of masked face detection approach in video analytics’, Conference on Advances in Signal Processing, Pune, India, 2016.

P. P D, P. Nath Singh, ‘Masked & Unmasked Face Recognition Using Support Vector Machine Classifier’, International Conference on Mobile Networks and Wireless Communications, Tumkur, Karnataka, India, 2021.

B. Kocacinar, et al., ‘A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System’, in: IEEE Access, 10, 2022.

L. Cimmino, et al., ‘M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis’, in: IEEE Access, 10, 2022.

NQ. Dao, et al., ‘Management of Video Surveillance for Smart Cities’, in: Handbook of Smart Cities, Springer, Cham, 2018.

M. Pudyel, M. Atay, ‘An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms’, SoutheastCon 2023, Orlando, FL, USA, 2023.

S. Hao, C. Chen, Z. Chen, K.-Y. K. Wong, ‘A Unified Framework for Masked and Mask-Free Face Recognition Via Feature Rectification’, International Conference on Image Processing, Bordeaux, France, 2022.

W. Lin, et al., ‘Masked Face Recognition with Qaudruplet Loss’, International Conference on Image Processing and Computer Applications, Changchun, China, 2023.

M. Zhang, R. Liu, D. Deguchi, H. Murase, ‘Masked Face Recognition With Mask Transfer and Self-Attention Under the COVID-19 Pandemic’, in: IEEE Access, 10, 2022.

M. Mobaraki, et al., ‘Masked Face Recognition Using Convolutional Neural Networks and Similarity Analysis’, 24th International Conference on Digital Signal Processing, Rhodes, Greece, 2023.

Y. Kondratenko, et al., ‘Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing’, in: Sensors, 22(3), 2022.

H. Wang, et al., ‘Research on Smooth Edge Feature Recognition Method for Aerial Image Segmentation’, 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Beijing, China, 2022.

S. Zhang, ‘Character Recognition of Historical and Cultural Relics Based on Digital Image Processing’, 5th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, 2021.

W. Yu, et al., ‘Application Research of Image Feature Recognition Algorithm in Visual Image Recognition’, Conference on Telecommunications, Optics and Computer Science, Dalian, China, 2022.

T. Biloborodova, et al., ‘ECG Classification Using Combination of Linear and Non-Linear Features with Neural Network’, in: Studies in Health Technology and Informaticsthis link is disabled, 2022.

B. Brosnan, I. Skarga-Bandurova, T. Biloborodova, I. Skarha-Bandurov, ‘An Integrated Approach to Automated Diagnosis of Cervical Intraepithelial Neoplasia in Digital Histology Images’, Studies in Health Technology and Informaticsthis link is disabled, 2023.

V. Alekseeva, et al., ‘Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation’, in: Electronics, 12(5), 2023.

M. Tetiana, Y. Kondratenko, I. Sidenko, G. Kondratenko, ‘Computer vision mobile system for education using augmented reality technology’, in: Journal of Mobile Multimedia, 17(4), 2021.

O. Striuk, et al., ‘Implementation of Generative Adversarial Networks in Mobile Applications for Image Data Enhancement’, in: Journal of Mobile Multimedia, 19(3), 2023.

C. Aggarwal, Neural Networks and Deep Learning, Springer, Cham, 2023.

T. Adar, E. K. Delice, O. Delice, ‘Detection of COVID-19 From A New Dataset Using MobileNetV2 and ResNet101V2 Architectures’, Medical Technologies Congress, Antalya, Turkey, 2022.

U. Kulkarni, et al., ‘Facial Key points Detection using MobileNetV2 Architecture’, 8th International Conference for Convergence in Technology, Lonavla, India, 2023.

A. B. Handoko, et al., ‘Evaluation of YOLO-X and MobileNetV2 as Face Mask Detection Algorithms’, Industrial Electronics and Applications Conference, Kuala Lumpur, Malaysia, 2022.

O. Striuk, Y. Kondratenko, I. Sidenko, A. Vorobyova, ‘Generative Adversarial Neural Network for Creating Photorealistic Images’, IEEE 2nd International Conference on Advanced Trends in Information Theory, Kyiv, Ukraine, 2020.

I. Sova, I. Sidenko, Y. Kondratenko, ‘Machine Learning Technology for Neoplasm Segmentation on Brain MRI Scans’, CEUR Workshop Proceedings, PhD Symposium at ICT in Education, Research, and Industrial Applications, 2791, Kharkiv, Ukraine, 2020.

I. Khortiuk, G. Kondratenko, I. Sidenko, Y. Kondratenko, ‘Scoring System Based on Neural Networks for Identification of Factors in Image Perception’, CEUR Workshop Proceedings, 4th International Conference on Computational Linguistics and Intelligent Systems, 2604, Lviv, Ukraine, 2020.

N. Lidströmer, H. Ashrafian (Eds), ‘Artificial Intelligence in Medicine’, Springer, Cham, 2022.

V.M. Kuntsevich, et al. (Eds), ‘Control Systems: Theory and Applications’, River Publishers, Gistrup, Delft, 2018.

R. Duro, et al. (Eds), ‘Advances in intelligent robotics and collaborative automation’, River Publishers, Aalborg, 2015.

V. Lytvyn, et al., ‘An intelligent system of the content relevance at the example of films according to user needs’, International Workshop on Information-Communication Technologies and Embedded Systems, ICT and ES, 2516, 2019.

S. Kryvyi, O. Grinenko, V. Opanasenko, ‘Logical Approach to the Research of Properties of Software Engineering Ecosystem,’ 11th International Conference on Dependable Systems, Services and Technologies, Kyiv, Ukraine, 2020.

S. Putatunda, ‘Practical Machine Learning for Streaming Data with Python’, Apress, Berkeley, CA, 2021.

N. Sanghi, ‘Deep Reinforcement Learning with Python’, Apress, Berkeley, CA, 2021.

J. Unpingco, ‘Python Programming for Data Analysis’, Springer, Cham, 2022.

A. I. Shevchenko, ‘Natural Human Intelligence – the Object of Research for Artificial Intelligence Creation,’ IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 2019.

A. I. Shevchenko, M. S. Klymenko, ‘Developing a Model of “Artificial Conscience”,’ IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT), Zbarazh, Ukraine, 2020.

T. Green, J. Labrecque, ‘A Guide to UX Design and Development’, Apress, Berkeley, CA, 2023.

M.E. Auer, T. Tsiatsos (Eds), ‘New Realities, Mobile Systems and Applications’, Springer, Cham, 2022.

M. Dakić, ‘Mobile App Development for Businesses’, Apress, Berkeley, CA, 2023.

J. Singh, D. Das, L. Kumar, A. Krishna (Eds), ‘Mobile Application Development: Practice and Experience’, Springer, Singapore, 2023.

A. Alnoor, K.K. Wah, A. Hassan (Eds), ‘Artificial Neural Networks and Structural Equation Modeling’, Springer, Singapore, 2022.

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Published

2024-05-06

How to Cite

Kondratenko, G., Sidenko, I., Saliutin, M., & Kondratenko, Y. (2024). Mobile Recognition of Image Components Based on Machine Learning Methods. Journal of Mobile Multimedia, 20(03), 699–726. https://doi.org/10.13052/jmm1550-4646.2038

Issue

Section

Control and Decision-making Systems with Mobile Applications

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