TY - GEN
T1 - Specialized Software Tool for Pattern Recognition of Biological Objects
AU - Kulik, Sergey D.
AU - Levin, Evgeny O.
N1 - Funding Information: Acknowledgement. This work was supported by Competitiveness Growth Program of the Federal Autonomous Educational Institution of Higher Education National Research Nuclear University MEPhI (Moscow Engineering Physics Institute). Publisher Copyright: © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This paper describes the results of work on developing the mobile application for recognition of faces and other biological objects. The application is designed with a focus on loading external machine learning models over the Internet, which allows you to change the model without making any modifications to the application. With such realization, the application can be used in many cases. For example, at carrying out conferences: organizers just need to train a model and send out the link for its downloading to all participants of the conference without any changes in source code. Participants will be able to find out the information about other members they are interested in, as well as contact them directly from the application, both by phone and by e-mail. Instructions are given for teaching your own face recognition model using the Microsoft Custom Vision cloud service, which allows you to train regardless of the power of your local computer. As an example, a classification model was trained and the following assessments of recognition quality indicators were obtained precision: 97.8% and recall: 95.8%. In our future work we consider adding the functionality of emotion recognition based on the pattern recognition algorithm, described in this paper.
AB - This paper describes the results of work on developing the mobile application for recognition of faces and other biological objects. The application is designed with a focus on loading external machine learning models over the Internet, which allows you to change the model without making any modifications to the application. With such realization, the application can be used in many cases. For example, at carrying out conferences: organizers just need to train a model and send out the link for its downloading to all participants of the conference without any changes in source code. Participants will be able to find out the information about other members they are interested in, as well as contact them directly from the application, both by phone and by e-mail. Instructions are given for teaching your own face recognition model using the Microsoft Custom Vision cloud service, which allows you to train regardless of the power of your local computer. As an example, a classification model was trained and the following assessments of recognition quality indicators were obtained precision: 97.8% and recall: 95.8%. In our future work we consider adding the functionality of emotion recognition based on the pattern recognition algorithm, described in this paper.
KW - Biological objects
KW - Cognitive technology
KW - Machine learning
KW - Mobile development
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85098166643&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-65596-9_22
DO - https://doi.org/10.1007/978-3-030-65596-9_22
M3 - Conference contribution
SN - 9783030655952
VL - 1310
T3 - Advances in Intelligent Systems and Computing
SP - 173
EP - 180
BT - Brain-Inspired Cognitive Architectures for Artificial Intelligence
A2 - Samsonovich, Alexei V.
A2 - Gudwin, Ricardo R.
A2 - Simões, Alexandre da
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020
Y2 - 10 November 2020 through 14 November 2020
ER -