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Online multimodal compression using pruning and knowledge distillation for iris recognition

Deep learning models have advanced to the forefront of image recognition tasks, resulting in high-performing but enormous neural networks with millions to billions of parameters. Yet, deploying these models in production systems imposes considerable memory limits. Hence, the research community is...

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Main Authors: Abdul Latif, Samihah, Sidek, Khairul Azami, Abu Bakar, Eddyzulham, Hassan Abdalla Hashim, Aisha
Format: Article
Language:English
English
Published: Semarak Ilmu Sdn Bhd 2024
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/111162/3/111162_Online%20multimodal%20compression%20using%20pruning_SCOPUS.pdf
http://irep.iium.edu.my/111162/4/111162_Online%20multimodal%20compression%20using%20pruning.pdf
http://irep.iium.edu.my/111162/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index
https://doi.org/10.37934/araset.37.2.6881
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spelling my.iium.irep.1111622024-08-28T04:59:59Z http://irep.iium.edu.my/111162/ Online multimodal compression using pruning and knowledge distillation for iris recognition Abdul Latif, Samihah Sidek, Khairul Azami Abu Bakar, Eddyzulham Hassan Abdalla Hashim, Aisha TK7885 Computer engineering Deep learning models have advanced to the forefront of image recognition tasks, resulting in high-performing but enormous neural networks with millions to billions of parameters. Yet, deploying these models in production systems imposes considerable memory limits. Hence, the research community is increasingly aware of the need for compression strategies that can reduce the number of model parameters and their resource requirement. Current compression techniques for deep learning models have limitations in efficiency and effectiveness, indicating that more research is required to develop more efficient and practical techniques capable of balancing the trade-offs between compression rate, computational cost, and accuracy. This study proposed a multimodal method by combining multimodal Pruning and Knowledge Distillation techniques for compressing the iris recognition model, which is the size constraint for many image recognition models. To maintain accuracy while shrinking the model’s size, the models are trained, compressed, and further retrained in the downstream job. The analysis includes both fully connected and convolutional layers. Experimentally, the findings show that the proposed technique can achieve 91% accuracy, the same as the existing or original model. Besides that, the model compression can reduce the size of the model almost six times, from 529MB to 90MB, which is a significantly reduced rate. The primary outcome of this study is developing a CNN lightweight model for iris recognition technology that can be used on mobile devices and is resource constrained. Semarak Ilmu Sdn Bhd 2024-01-16 Article PeerReviewed application/pdf en http://irep.iium.edu.my/111162/3/111162_Online%20multimodal%20compression%20using%20pruning_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/111162/4/111162_Online%20multimodal%20compression%20using%20pruning.pdf Abdul Latif, Samihah and Sidek, Khairul Azami and Abu Bakar, Eddyzulham and Hassan Abdalla Hashim, Aisha (2024) Online multimodal compression using pruning and knowledge distillation for iris recognition. Journal of Advanced Research in Applied Sciences and Engineering Technology, 37 (2). pp. 68-81. ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index https://doi.org/10.37934/araset.37.2.6881
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Abdul Latif, Samihah
Sidek, Khairul Azami
Abu Bakar, Eddyzulham
Hassan Abdalla Hashim, Aisha
Online multimodal compression using pruning and knowledge distillation for iris recognition
description Deep learning models have advanced to the forefront of image recognition tasks, resulting in high-performing but enormous neural networks with millions to billions of parameters. Yet, deploying these models in production systems imposes considerable memory limits. Hence, the research community is increasingly aware of the need for compression strategies that can reduce the number of model parameters and their resource requirement. Current compression techniques for deep learning models have limitations in efficiency and effectiveness, indicating that more research is required to develop more efficient and practical techniques capable of balancing the trade-offs between compression rate, computational cost, and accuracy. This study proposed a multimodal method by combining multimodal Pruning and Knowledge Distillation techniques for compressing the iris recognition model, which is the size constraint for many image recognition models. To maintain accuracy while shrinking the model’s size, the models are trained, compressed, and further retrained in the downstream job. The analysis includes both fully connected and convolutional layers. Experimentally, the findings show that the proposed technique can achieve 91% accuracy, the same as the existing or original model. Besides that, the model compression can reduce the size of the model almost six times, from 529MB to 90MB, which is a significantly reduced rate. The primary outcome of this study is developing a CNN lightweight model for iris recognition technology that can be used on mobile devices and is resource constrained.
format Article
author Abdul Latif, Samihah
Sidek, Khairul Azami
Abu Bakar, Eddyzulham
Hassan Abdalla Hashim, Aisha
author_facet Abdul Latif, Samihah
Sidek, Khairul Azami
Abu Bakar, Eddyzulham
Hassan Abdalla Hashim, Aisha
author_sort Abdul Latif, Samihah
title Online multimodal compression using pruning and knowledge distillation for iris recognition
title_short Online multimodal compression using pruning and knowledge distillation for iris recognition
title_full Online multimodal compression using pruning and knowledge distillation for iris recognition
title_fullStr Online multimodal compression using pruning and knowledge distillation for iris recognition
title_full_unstemmed Online multimodal compression using pruning and knowledge distillation for iris recognition
title_sort online multimodal compression using pruning and knowledge distillation for iris recognition
publisher Semarak Ilmu Sdn Bhd
publishDate 2024
url http://irep.iium.edu.my/111162/3/111162_Online%20multimodal%20compression%20using%20pruning_SCOPUS.pdf
http://irep.iium.edu.my/111162/4/111162_Online%20multimodal%20compression%20using%20pruning.pdf
http://irep.iium.edu.my/111162/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index
https://doi.org/10.37934/araset.37.2.6881
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score 13.211869

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