Tiny hand gesture recognition without localization via a deep convolutional network

Bao, Peijun and Maqueda Nieto, Ana Isabel and Blanco Adán, Carlos Roberto del and García Santos, Narciso (2017). Tiny hand gesture recognition without localization via a deep convolutional network. "IEEE Transactions on Consumer Electronics", v. 63 (n. 3); pp. 251-257. ISSN 0098-3063. https://doi.org/10.1109/TCE.2017.014971.

Description

Title: Tiny hand gesture recognition without localization via a deep convolutional network
Author/s:
  • Bao, Peijun
  • Maqueda Nieto, Ana Isabel
  • Blanco Adán, Carlos Roberto del
  • García Santos, Narciso
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Consumer Electronics
Date: 2017
ISSN: 0098-3063
Volume: 63
Subjects:
Freetext Keywords: Deep learning; hand gesture recognition; human-machine interface; mobile phone; neural network; no localization; TV
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Visual hand-gesture recognition is being increasingly desired for human-computer interaction interfaces. In many applications, hands only occupy about 10% of the image, whereas the most of it contains background, human face, and human body. Spatial localization of the hands in such scenarios could be a challenging task and ground truth bounding boxes need to be provided for training, which is usually not accessible. However, the location of the hand is not a requirement when the criteria is just the recognition of a gesture to command a consumer electronics device, such as mobiles phones and TVs. In this paper, a deep convolutional neural network is proposed to directly classify hand gestures in images without any segmentation or detection stage that could discard the irrelevant not-hand areas. The designed hand-gesture recognition network can classify seven sorts of hand gestures in a user-independent manner and on real time, achieving an accuracy of 97.1% in the dataset with simple backgrounds and 85.3% in the dataset with complex backgrounds.

More information

Item ID: 50817
DC Identifier: http://oa.upm.es/50817/
OAI Identifier: oai:oa.upm.es:50817
DOI: 10.1109/TCE.2017.014971
Official URL: https://ieeexplore.ieee.org/document/8103373/
Deposited by: Memoria Investigacion
Deposited on: 26 Jun 2018 16:27
Last Modified: 26 Jun 2018 16:27
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