Difference between revisions of "Smartphone Facial Recognition"
From Psyc 40 Wiki
| Line 3: | Line 3: | ||
'''Note: This page is incomplete.''' | '''Note: This page is incomplete.''' | ||
| − | Recognizing faces is trivial for humans but has been a difficult task for computers until recently | + | Recognizing faces is trivial for humans but has been a difficult task for computers until recently {{cite web |url=https://machinelearningmastery.com/introduction-to-deep-learning-for-face-recognition/ |title=A Gentle Introduction to Deep Learning for Face Recognition |last=Browlee |first=Jason |publisher=Machine Learning Mastery |date=July 5, 2019 |website=Machine Learning Mastery |access-date=November 14, 2022}} |
| − | + | ||
| + | |||
| + | |||
| + | |||
| + | |||
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 ([https://machinelearning.apple.com/research/face-detection#1]). Accordingly, smartphone manufacturers have taken to creating processors with dedicated neural engines for deep learning tasks ([https://semiconductor.samsung.com/processor/mobile-processor/exynos-9-series-9810/]) as well as creating simpler and more compact models that mimic the behavior of more complex models ([https://machinelearning.apple.com/research/face-detection#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 ([https://machinelearning.apple.com/research/face-detection#1]). Accordingly, smartphone manufacturers have taken to creating processors with dedicated neural engines for deep learning tasks ([https://semiconductor.samsung.com/processor/mobile-processor/exynos-9-series-9810/]) as well as creating simpler and more compact models that mimic the behavior of more complex models ([https://machinelearning.apple.com/research/face-detection#1)]. | ||
== References == | == References == | ||
{{Reflist}} | {{Reflist}} | ||
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 Template:Cite web
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 ([1]). Accordingly, smartphone manufacturers have taken to creating processors with dedicated neural engines for deep learning tasks ([2]) as well as creating simpler and more compact models that mimic the behavior of more complex models ([3].
