Low-cost system for gender recognition using convolutional neural network

Kód výsledku v 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>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Low-cost system for gender recognition using convolutional neural network

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Low-cost system for gender recognition using convolutional neural network

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

7

Strana od-do

6316-6322

Název nakladatele

International Business Information Management Association-IBIMA

Místo vydání

Norristown

Místo konání akce

Madrid

Datum konání akce

13. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000556337408049