Enhanced gesture-based human-computer interaction through a compressive sensing reduction scheme of very large and efficient depth feature descriptors

Mantecón del Valle, Tomás and Mantecón Jené, Ana and Blanco Adán, Carlos Roberto del and Jaureguizar Núñez, Fernando and García Santos, Narciso (2015). Enhanced gesture-based human-computer interaction through a compressive sensing reduction scheme of very large and efficient depth feature descriptors. In: "12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)", 25/08/2015 - 28/08/2015, Karlsruhe, Germany. pp. 1-6. https://doi.org/10.1109/AVSS.2015.7301804.

Description

Title: Enhanced gesture-based human-computer interaction through a compressive sensing reduction scheme of very large and efficient depth feature descriptors
Author/s:
  • Mantecón del Valle, Tomás
  • Mantecón Jené, Ana
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Item Type: Presentation at Congress or Conference (Article)
Event Title: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Event Dates: 25/08/2015 - 28/08/2015
Event Location: Karlsruhe, Germany
Title of Book: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Date: 2015
Subjects:
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

In this paper, a hand gesture-based recognition system is presented with the aim of recognizing finger-spelling using the American Sign Language. The solution makes use of the depth imagery acquired by the new Kinect 2 sensor that provides more depth resolution. The main novelty is the introduction of a Compressive Sensing step to reduce the dimension of a depth-based feature descriptor, called Depth Spatiograms of Quantized Patterns, which is very discriminative, but also too large for its practical application. The system is composed by three steps: 1) depth-based feature descriptor computation that robustly characterizes the hand gesture; 2) Compressive Sensing based dimensionality reduction that shortens the previous highly discriminative but also large feature vector with almost no information lost; and 3) Support Vector Machine based classification that recognizes the performed hand gestures. Promising recognition results have been obtained in an American Sign Language based database.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412Enhanced 3DTVMinisterio de Economía y CompetitividadUnspecified
Government of SpainTEC2013-48453MR-UHDTVMinisterio de Economía y CompetitividadUnspecified

More information

Item ID: 42759
DC Identifier: http://oa.upm.es/42759/
OAI Identifier: oai:oa.upm.es:42759
DOI: 10.1109/AVSS.2015.7301804
Deposited by: Memoria Investigacion
Deposited on: 16 Jul 2016 09:55
Last Modified: 16 Jul 2016 09:55
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