A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing

Ali Boyali; Manolya Kavakli

doi:10.1109/ICIEA.2012.6360730

A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing

Ali Boyali^*, Manolya Kavakli

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

18 Citations (Scopus)

Abstract

Compressed Sensing (CS) and Sparse Representation (SR) influenced the ways of signals are processed half a decade. The elegant solution to sparse signal recovery problem has found ground in several research fields such as machine learning and pattern recognition. The use of sparse representation and the solution of equations using ℓ₁ minimization were utilized for face recognition problem under varying illumination and occlusion. Afterwards the idea was applied in biometrics to classify iris data. Similar to those studies, we use the discriminating nature of sparsity for the signals acquired in various signal domains and apply them to gesture recognition problem. The proposed algorithm in this context gives accurate recognition results over a recognition rate of 99% for user independent and 100% for user dependent gesture sets for fairly rich gesture dictionaries.

Original language	English
Title of host publication	Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	243-249
Number of pages	7
ISBN (Print)	9781457721175
DOIs	https://doi.org/10.1109/ICIEA.2012.6360730
Publication status	Published - 2012
Event	2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 - Singapore, Singapore Duration: 18 Jul 2012 → 20 Jul 2012

Other

Other	2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012
Country/Territory	Singapore
City	Singapore
Period	18/07/12 → 20/07/12

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Boyali, A., & Kavakli, M. (2012). A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing. In Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 (pp. 243-249). [6360730] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICIEA.2012.6360730

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title = "A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing",

abstract = "Compressed Sensing (CS) and Sparse Representation (SR) influenced the ways of signals are processed half a decade. The elegant solution to sparse signal recovery problem has found ground in several research fields such as machine learning and pattern recognition. The use of sparse representation and the solution of equations using ℓ1 minimization were utilized for face recognition problem under varying illumination and occlusion. Afterwards the idea was applied in biometrics to classify iris data. Similar to those studies, we use the discriminating nature of sparsity for the signals acquired in various signal domains and apply them to gesture recognition problem. The proposed algorithm in this context gives accurate recognition results over a recognition rate of 99% for user independent and 100% for user dependent gesture sets for fairly rich gesture dictionaries.",

author = "Ali Boyali and Manolya Kavakli",

year = "2012",

doi = "10.1109/ICIEA.2012.6360730",

language = "English",

isbn = "9781457721175",

pages = "243--249",

booktitle = "Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012 ; Conference date: 18-07-2012 Through 20-07-2012",

}

Boyali, A & Kavakli, M 2012, A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing. in Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012., 6360730, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 243-249, 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012, Singapore, Singapore, 18/07/12. https://doi.org/10.1109/ICIEA.2012.6360730

A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing. / Boyali, Ali; Kavakli, Manolya.
Proceedings of the 2012 7th IEEE Conference on Industrial Electronics and Applications, ICIEA 2012. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2012. p. 243-249 6360730.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - Compressed Sensing (CS) and Sparse Representation (SR) influenced the ways of signals are processed half a decade. The elegant solution to sparse signal recovery problem has found ground in several research fields such as machine learning and pattern recognition. The use of sparse representation and the solution of equations using ℓ1 minimization were utilized for face recognition problem under varying illumination and occlusion. Afterwards the idea was applied in biometrics to classify iris data. Similar to those studies, we use the discriminating nature of sparsity for the signals acquired in various signal domains and apply them to gesture recognition problem. The proposed algorithm in this context gives accurate recognition results over a recognition rate of 99% for user independent and 100% for user dependent gesture sets for fairly rich gesture dictionaries.

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A robust gesture recognition algorithm based on sparse representation, random projections and compressed sensing

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