Hand gesture recognition using ensemble method
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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Brac University
2023
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| Online toegang: | http://hdl.handle.net/10361/21931 |
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10361-21931 |
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10361-219312023-12-06T21:02:30Z Hand gesture recognition using ensemble method Kowsar, Sahib Chowdhury, Mahzabin Mahmud, MD Safin Haque, Shahbaj Shafin Shifa, Asaka Akther Ahmed , Tanvir Nahim, Nabuat Zaman Department of Computer Science and Engineering, Brac University Pattern matching Feature extraction SSTCN SL-GCN Pipeline Transfer learning Histogram matching Artificial intelligence Optical pattern recognition Computer vision This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 27-28). Even though things have improved much more over the last century in terms of com- munication, there still is a glaring amount of communication gap between the hearing majority and the deaf community due to the lack of resources in the field. Real time hand gesture recognition development tries to tear down this communication barrier and open a new common ground for everyone and hand gesture recognition plays a vital role in human-computer interaction as well. There are several ideas on how to build a model to properly recognize sign languages. The models differ based on the computation time it takes, the algorithms used and if it can be used in real time or not. In this work we take a thorough analysis of real-time hand gesture recognition models and proposes a pipeline-based approach to select the best-performing model as the final output. We chose to work with four datasets that are being used here for comparison, SLR500, AUTSL-226, WLASL2000 and WLASL100. The goal here is to find a way to overcome the limitations of data scarcity in the field along with the imbalance in classification problems. We work with video inputs to run them through different modalities simultaneously through a set of pipelines to produce outputs which would then be used in getting the final classification result by using the core idea of generating the final output of the ensemble technique. Various data pre-processing techniques are used such as regularization, histogram equalization etc. to minimize the varying skin tone bias to make it a more inclusive model for better classification and improved accuracy score. The existing models have no way to deal with biases encountered in sign language detection and we take various dif- ferent approaches to overcome such limitations. In general pristine cases for around 500 classes the model performs 96.32 percent in terms of top-1 accuracy. Sahib Kowsar Mahzabin Chowdhury MD Safin Mahmud Shahbaj Shafin Haque Asaka Akther Shifa B.Sc. in Computer Science and Engineering 2023-12-06T06:40:36Z 2023-12-06T06:40:36Z 2023 2023-05 Thesis ID 19301096 ID 19301084 ID 19301231 ID 19101566 ID 19301069 http://hdl.handle.net/10361/21931 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 28 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Pattern matching Feature extraction SSTCN SL-GCN Pipeline Transfer learning Histogram matching Artificial intelligence Optical pattern recognition Computer vision |
| spellingShingle |
Pattern matching Feature extraction SSTCN SL-GCN Pipeline Transfer learning Histogram matching Artificial intelligence Optical pattern recognition Computer vision Kowsar, Sahib Chowdhury, Mahzabin Mahmud, MD Safin Haque, Shahbaj Shafin Shifa, Asaka Akther Hand gesture recognition using ensemble method |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Ahmed , Tanvir |
| author_facet |
Ahmed , Tanvir Kowsar, Sahib Chowdhury, Mahzabin Mahmud, MD Safin Haque, Shahbaj Shafin Shifa, Asaka Akther |
| format |
Thesis |
| author |
Kowsar, Sahib Chowdhury, Mahzabin Mahmud, MD Safin Haque, Shahbaj Shafin Shifa, Asaka Akther |
| author_sort |
Kowsar, Sahib |
| title |
Hand gesture recognition using ensemble method |
| title_short |
Hand gesture recognition using ensemble method |
| title_full |
Hand gesture recognition using ensemble method |
| title_fullStr |
Hand gesture recognition using ensemble method |
| title_full_unstemmed |
Hand gesture recognition using ensemble method |
| title_sort |
hand gesture recognition using ensemble method |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21931 |
| work_keys_str_mv |
AT kowsarsahib handgesturerecognitionusingensemblemethod AT chowdhurymahzabin handgesturerecognitionusingensemblemethod AT mahmudmdsafin handgesturerecognitionusingensemblemethod AT haqueshahbajshafin handgesturerecognitionusingensemblemethod AT shifaasakaakther handgesturerecognitionusingensemblemethod |
| _version_ |
1814307294725275648 |