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)
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Abstract
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%.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Artificial Intelligence, Artificial Neural Networks, Convolutional Neural Networks, GPU, CPU, TPU |
| Subjects: | T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology Faculty of Manufacturing and Mechatronic Engineering Technology Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 14 Aug 2023 02:59 |
| Last Modified: | 16 Jan 2024 03:05 |
| URI: | http://umpir.ump.edu.my/id/eprint/38353 |
| Download Statistic: | View Download Statistics |
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