Difference between revisions of "Smartphone Facial Recognition"
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Revision as of 23:31, 21 October 2022
By Kenneth Wu
Note: This page is incomplete.
Recognizing faces is trivial for humans but has been a difficult task for computers until recently. [1] The deep learning models popular in modern facial recognition systems use much more memory, disk storage, and computational resources than traditional computer vision, presenting significant challenges to the limited hardware capabilities of smartphones. [2] Accordingly, smartphone manufacturers have taken to creating processors with dedicated neural engines for deep learning tasks [3] as well as creating simpler and more compact models that mimic the behavior of more complex models. [2]
Contents
Model
History
Applications
References
- ↑ Brownlee, J. (2019, July 5). A gentle introduction to deep learning for face recognition. Machine Learning Mastery. Retrieved November 14, 2022, from https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/
- ↑ 2.0 2.1 Computer Vision Machine Learning Team. (2017, November). An on-device deep neural network for face detection. Apple Machine Learning Research. Retrieved November 14, 2022, from https://machinelearning.apple.com/research/face-detection#1
- ↑ Samsung. (2018). Exynos 9810: Mobile Processor. Samsung Semiconductor Global. Retrieved November 14, 2022, from https://semiconductor.samsung.com/processor/mobile-processor/exynos-9-series-9810/
