Static hand gesture recognition using stacked Denoising Sparse Autoencoders

With the advent of personal computers, humans have always wanted to communicate with them in either their natural language or by using gestures. This gave birth to the field of Human Computer Interaction and its subfield Automatic Sign Language Recognition. This paper proposes the method of automati...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2014 Seventh International Conference on Contemporary Computing (IC3) s. 99 - 104
Hlavní autoři: Kumar, Varun, Nandi, G. C., Kala, Rahul
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2014
Témata:
Autoencoders
Cost function
Deep learning
Feature extraction
Gesture recognition
Neurons
Noise reduction
Static hand gesture recognition
Training
Vectors
ISBN:1479951722, 9781479951727
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract With the advent of personal computers, humans have always wanted to communicate with them in either their natural language or by using gestures. This gave birth to the field of Human Computer Interaction and its subfield Automatic Sign Language Recognition. This paper proposes the method of automatic feature extraction of the images of hand. These extracted features are then used to train the Softmax classifier to classify them into 20 classes. Five stacked Denoising Sparse Autoencoders (DSAE) trained in unsupervised fashion are used to extract features from image. The proposed architecture is trained and tested on a standard dataset [1] which was extended by adding random jitters such as rotation and Gaussian noise. The performance of the proposed architecture is 83% which is better than shallow Neural Network trained on manual hand-engineered features called Principal Components which is used as a benchmark.
AbstractList With the advent of personal computers, humans have always wanted to communicate with them in either their natural language or by using gestures. This gave birth to the field of Human Computer Interaction and its subfield Automatic Sign Language Recognition. This paper proposes the method of automatic feature extraction of the images of hand. These extracted features are then used to train the Softmax classifier to classify them into 20 classes. Five stacked Denoising Sparse Autoencoders (DSAE) trained in unsupervised fashion are used to extract features from image. The proposed architecture is trained and tested on a standard dataset [1] which was extended by adding random jitters such as rotation and Gaussian noise. The performance of the proposed architecture is 83% which is better than shallow Neural Network trained on manual hand-engineered features called Principal Components which is used as a benchmark.
Author Nandi, G. C.
Kumar, Varun
Kala, Rahul
Author_xml – sequence: 1
  givenname: Varun
  surname: Kumar
  fullname: Kumar, Varun
  email: varun.k.iiit@gmail.com
  organization: Robot. & Artificial Intell. Lab., Indian Inst. of Inf. Technol., Allahabad, India
– sequence: 2
  givenname: G. C.
  surname: Nandi
  fullname: Nandi, G. C.
  organization: Robot. & Artificial Intell. Lab., Indian Inst. of Inf. Technol., Allahabad, India
– sequence: 3
  givenname: Rahul
  surname: Kala
  fullname: Kala, Rahul
  organization: Robot. & Artificial Intell. Lab., Indian Inst. of Inf. Technol., Allahabad, India
BookMark eNpFj81KAzEURiMqaGv3gpu8wNTc_JplGa0WCy7afckkd8agJmWSWfj2ghZcfZyzOPDNyEXKCQm5BbYEYPZ-04olZyCX-sEaUOqMzEAaaxUYwc7_gfMrsigldoxro6U2cE1ed9XV6Om7S4EOWOo0Ih3R5yHFGnOiU4lpoKU6_4GBPmLK8dfsjm4sSFdTzZh8DjiWG3LZu8-Ci9POyX79tG9fmu3b86ZdbZtoWW3Ao0SBQQPXrpNeehZEJ1nfGeeZNwpkEEGr4KRm4IyFXiuF3HgueqN6MSd3f9mIiIfjGL_c-H04fRc_2fRQEQ
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/IC3.2014.6897155
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 1479951730
1479951714
9781479951710
9781479951734
EndPage 104
ExternalDocumentID 6897155
Genre orig-research
GroupedDBID 6IE
6IL
ALMA_UNASSIGNED_HOLDINGS
CBEJK
RIB
RIC
RIE
RIL
ID FETCH-LOGICAL-i90t-1ce4e3ed6126ab4c4c0d3b40fb7ac0c7514d3d65da4601a791f655e27c23f75f3
IEDL.DBID RIE
ISBN 1479951722
9781479951727
IngestDate Wed Jun 26 19:23:45 EDT 2024
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i90t-1ce4e3ed6126ab4c4c0d3b40fb7ac0c7514d3d65da4601a791f655e27c23f75f3
PageCount 6
ParticipantIDs ieee_primary_6897155
PublicationCentury 2000
PublicationDate 2014-Aug.
PublicationDateYYYYMMDD 2014-08-01
PublicationDate_xml – month: 08
  year: 2014
  text: 2014-Aug.
PublicationDecade 2010
PublicationTitle 2014 Seventh International Conference on Contemporary Computing (IC3)
PublicationTitleAbbrev IC3
PublicationYear 2014
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssib026764671
Score 1.6035655
Snippet With the advent of personal computers, humans have always wanted to communicate with them in either their natural language or by using gestures. This gave...
SourceID ieee
SourceType Publisher
StartPage 99
SubjectTerms Autoencoders
Cost function
Deep learning
Feature extraction
Gesture recognition
Neurons
Noise reduction
Static hand gesture recognition
Training
Vectors
Title Static hand gesture recognition using stacked Denoising Sparse Autoencoders
URI https://ieeexplore.ieee.org/document/6897155
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA7b8OBJZRN_k4NHu7Vp2ixHmQ5FGQOH7DaSlxfppR2z9e83ybqJ4MVbE2goLw3ve3nf9x4ht1YyCxkzkfUSd-5CiEgqxSOmgRmTSGlDM5j3VzGbjZdLOe-Qu70WBhED-QyH_jHk8k0Fjb8qG-VjKZz_65KuEPlWq7X7d1gucnfmk6Dd8kXOnGNmu5JO7Vjs0pSxHD1PUs_r4sN2zV_NVYJvmR7976uOyeBHpEfne_dzQjpY9smLB48FUH8fTn3qqNkg3ZOEqpJ6nvsHdZDQnV5DH7CsijDztnYRLtL7pq58aUtPbx6QxfRxMXmK2n4JUSHjOkoAOaZoHGbJlebAITap5rHVQkEMwkEjk5o8M4q7KEwJmdg8y5AJYKkVmU1PSa-sSjwjlIER7hUHdWLNBaBUYAxojVoKpVV8TvreDqv1tiLGqjXBxd_Tl-TQm3pLm7sivXrT4DU5gK-6-NzchG38BtkPnPI
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFG4QTfSkBoz4swePDrquW-nRoAQCEhKJ4Ub649XsshHc_Pttx8CYePG2NlmzvK553-v7vvcQerCCWh1TE1gvcWcuhAiElCygSlNjQiFs1Qzmfcpns_5yKeYN9LjXwgBART6Drn-scvkm16W_KuslfcGd_ztAhzFjlGzVWru_hyY8cac-rNRbvsyZc810V9SpHvNdopKI3ngQeWYX69ar_mqvUnmX4en_vusMtX9keni-d0DnqAFZC008fEw19jfi2CePyg3gPU0oz7Bnun9gBwrd-TX4GbI8rWbe1i7GBfxUFrkvbukJzm20GL4sBqOg7pgQpIIUQaiBQQTGoZZEKqaZJiZSjFjFpSaaO3BkIpPERjIXh0kuQpvEMVCuaWR5bKML1MzyDC4Rptpw94oDO0QxrkFIbYxWCpTgUknSQS1vh9V6WxNjVZvg6u_pe3Q8WrxOV9PxbHKNTrzZtyS6G9QsNiXcoiP9VaSfm7tqS78BNIugOQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2014+Seventh+International+Conference+on+Contemporary+Computing+%28IC3%29&rft.atitle=Static+hand+gesture+recognition+using+stacked+Denoising+Sparse+Autoencoders&rft.au=Kumar%2C+Varun&rft.au=Nandi%2C+G.+C.&rft.au=Kala%2C+Rahul&rft.date=2014-08-01&rft.pub=IEEE&rft.isbn=1479951722&rft.spage=99&rft.epage=104&rft_id=info:doi/10.1109%2FIC3.2014.6897155&rft.externalDocID=6897155
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781479951727/lc.gif&client=summon&freeimage=true
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781479951727/mc.gif&client=summon&freeimage=true
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781479951727/sc.gif&client=summon&freeimage=true