IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27200%2F22%3A10251683" target="_blank" >RIV/61989100:27200/22:10251683 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27240/22:10251683
Result on the web
<a href="https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=8472&" target="_blank" >https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=8472&</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5593/sgem2022/2.1/s07.06" target="_blank" >10.5593/sgem2022/2.1/s07.06</a>
Alternative languages
Result language
angličtina
Original language name
IMPLEMENTATION OF INTELLIGENT BIOMETRIC SYSTEM FOR FACE DETECTION AND CLASSIFICATION
Original language description
This article deals with the design and implementation of an intelligent biometric system that allows the detection and classification of a person's face from static image data and creates a system for evaluating its reliability. In its introductory part, it theoretically describes applied biometrics and biometric systems for security identification and user verification, and also deals with the theory of the description of algorithms for human face detection and recognition. Subsequently, the authors use the MATLAB programming language, which is highly optimized for modern processors and memory architectures, to focus on the implementation and testing of a biometric system using Viola-Jones algorithms and a convolutional neural network with a pre-trained network NetNet. Convolutional neural networks (CNN) are the most recognized and popular deep-learning neural networks, which are based on layers that perform two-dimensional (2D) convolution of input data with learned filters. In the final part there is a discussion where, based on the results of testing, the robustness and efficiency of the proposed intelligent biometric system is objectively evaluated. The results allow for the continued development of other pre-trained artificial neural networks, variable implementations for facial recognition, but also other things, such as the recognition of potentially dangerous people.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM. Volume 22, Issue 2.1
ISBN
978-619-7603-40-8
ISSN
1314-2704
e-ISSN
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Number of pages
8
Pages from-to
43-50
Publisher name
STEF92 Technology Ltd.
Place of publication
Sofia
Event location
Albena
Event date
Jul 2, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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