Mobilenetv3: a deep learning technique for human face expressions identification-Reference-Cited by-同舟云学术

Mobilenetv3: a deep learning technique for human face expressions identification

Published:2023-08 Issue:6 Volume:15 Page:3229-3243
ISSN:2511-2104
Container-title:International Journal of Information Technology
language:en
Short-container-title:Int. j. inf. tecnol.

Author:

Prasad S. Babu Rajendra,Chandana B. Sai

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s41870-023-01380-x.pdf

Reference24 articles.

1. Hossain S, Umer S, Asari V, Rout RK (2021) A unified framework of deep learning-based facial expression recognition system for diversified applications. Appl Sci 11(19):9174

2. Bashbaghi S, Granger E, Sabourin R, Parchami M (2019) Deep learning architectures for face recognition in video surveillance. In: Deep learning in object detection and recognition. Springer, Singapore, pp 133–154

3. Lee JM, An YE, Bak E, Pan S (2022) Improvement of negative emotion recognition in visible images enhanced by thermal imaging. Sustainability 14(22):15200

4. Lai YH, Chang YC, Tsai CW, Lin CH, Chen MY (2021) Data fusion analysis for attention-deficit hyperactivity disorder emotion recognition with thermal image and internet of things devices. Softw Pract Exp 51(3):595–606

5. Pons G, Ali AE, Cesar P (2020) ET-CycleGAN: generating thermal images from images in the visible spectrum for facial emotion recognition. In: Companion publication of the 2020 international conference on multimodal interaction, pp 87–91

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