Low-cost system for gender recognition using convolutional neural network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F19%3A39914546" target="_blank" >RIV/00216275:25410/19:39914546 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Low-cost system for gender recognition using convolutional neural network
Original language description
Gender recognition of human face images is an important task in computer vision. The characters with the greatest gender diversity are the face and the pelvis, so the article uses face images to determine the gender. There are many reasons to automatically determine gender. One of them is visual surveillance. Other applications includes marketing, intelligent user interfaces, demographic studies. This paper presents a modern approach in identifying gender by using drones and specialized neural networks. This paper uses UAV data, it is a low cost data acquisition solution. The data has a very high resolution, so it is possible to obtain face cut-outs. Face cut-outs are then used to determine gender. The convolutional neural network AlexNet is used for classification. The system does not require any pre-processing and features extraction before classification. The experiments were performed on a database of 500 face images. Duplication of data was minimized due to the flight planned in advance. The obtained accuracy of gender recognition is 95.14%, 70% data was used for training.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 34th International Business Information Management Association Conference. Vision 2025: Education Excellence and Management of Innovations through Sustainable Economic Competitive Advantage, IBIMA 2019
ISBN
978-0-9998551-3-3
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
6316-6322
Publisher name
International Business Information Management Association-IBIMA
Place of publication
Norristown
Event location
Madrid
Event date
Nov 13, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000556337408049