Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU
In light of the increasing computational capacity provided by Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), all of these were designed to speed up deep learning workloads, and the fact that this iteration of human-computer interaction is becom...
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| Format: | Article |
| Language: | English |
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Semarak Ilmu Publishing
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38353/ http://umpir.ump.edu.my/id/eprint/38353/1/Analysis%20of%20Convolutional%20Neural%20Networks%20for%20Facial%20Expression.pdf |
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| author | Anbananthan Pillai, Munanday Norazlianie, Sazali Wan Sharuzi, Wan Harun K., Kadirgama Ahmad Shahir, Jamaludin |
| author_facet | Anbananthan Pillai, Munanday Norazlianie, Sazali Wan Sharuzi, Wan Harun K., Kadirgama Ahmad Shahir, Jamaludin |
| author_sort | Anbananthan Pillai, Munanday |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In light of the increasing computational capacity provided by Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), all of these were designed to speed up deep learning workloads, and the fact that this iteration of human-computer interaction is becoming more natural and social, it is clear that the field of human-computer interaction is poised for significant growth. The scientific community has found emotion recognition to be of tremendous interest and significance. Despite these advances, it is still desired that research into computational methods for identifying and recognizing emotions at the same ease as humans. This study uses Convolutional Neural Networks (CNN) for human emotion identification from facial expressions to delve deeper into this topic. The results demonstrated that training an Artificial Neural Networks (ANN) on GPUs might cut computational time by as much as 90% while accuracy could be raised up to 65%. |
| first_indexed | 2025-11-15T03:29:42Z |
| format | Article |
| id | ump-38353 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:29:42Z |
| publishDate | 2023 |
| publisher | Semarak Ilmu Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-383532024-01-16T03:05:16Z http://umpir.ump.edu.my/id/eprint/38353/ Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU Anbananthan Pillai, Munanday Norazlianie, Sazali Wan Sharuzi, Wan Harun K., Kadirgama Ahmad Shahir, Jamaludin T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering In light of the increasing computational capacity provided by Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Tensor Processing Units (TPUs), all of these were designed to speed up deep learning workloads, and the fact that this iteration of human-computer interaction is becoming more natural and social, it is clear that the field of human-computer interaction is poised for significant growth. The scientific community has found emotion recognition to be of tremendous interest and significance. Despite these advances, it is still desired that research into computational methods for identifying and recognizing emotions at the same ease as humans. This study uses Convolutional Neural Networks (CNN) for human emotion identification from facial expressions to delve deeper into this topic. The results demonstrated that training an Artificial Neural Networks (ANN) on GPUs might cut computational time by as much as 90% while accuracy could be raised up to 65%. Semarak Ilmu Publishing 2023-08 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/38353/1/Analysis%20of%20Convolutional%20Neural%20Networks%20for%20Facial%20Expression.pdf Anbananthan Pillai, Munanday and Norazlianie, Sazali and Wan Sharuzi, Wan Harun and K., Kadirgama and Ahmad Shahir, Jamaludin (2023) Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU. Journal of Advanced Research in Applied Sciences and Engineering Technology, 31 (3). pp. 50-67. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.31.3.5067 https://doi.org/10.37934/araset.31.3.5067 |
| spellingShingle | T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Anbananthan Pillai, Munanday Norazlianie, Sazali Wan Sharuzi, Wan Harun K., Kadirgama Ahmad Shahir, Jamaludin Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU |
| title | Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU |
| title_full | Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU |
| title_fullStr | Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU |
| title_full_unstemmed | Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU |
| title_short | Analysis of convolutional neural networks for facial expression recognition on GPU, TPU and CPU |
| title_sort | analysis of convolutional neural networks for facial expression recognition on gpu, tpu and cpu |
| topic | T Technology (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/38353/ http://umpir.ump.edu.my/id/eprint/38353/ http://umpir.ump.edu.my/id/eprint/38353/ http://umpir.ump.edu.my/id/eprint/38353/1/Analysis%20of%20Convolutional%20Neural%20Networks%20for%20Facial%20Expression.pdf |