Difference between revisions of "Smartphone Facial Recognition"
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Facial recognition systems are computer programs that match faces against a database [https://en.wikipedia.org/wiki/Facial_recognition_system]. A trivial task for humans, achieving high levels of accuracy has been difficult for computers until recently.<ref> 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/ </ref> Deep learning [https://en.wikipedia.org/wiki/Deep_learning] through the use of convolutional neural networks [https://en.wikipedia.org/wiki/Convolutional_neural_network] currently dominates the facial recognition field.<ref> Almabdy, S., & Elrefaei, L. (2019). Deep convolutional neural network-based approaches for face recognition. ''Applied Sciences'', 9(20), 4397. https://doi.org/10.3390/app9204397 </ref> However, deep learning uses much more memory, disk storage, and computational resources than traditional computer vision, presenting significant challenges to facial recognition with the limited hardware capabilities of smartphones.<ref name="apple"> 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 </ref> Accordingly, smartphone manufacturers have taken to using processors with dedicated neural engines for deep learning tasks <ref> 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/ </ref> as well as creating simpler and more compact models that mimic the behavior of more complex models.<ref name="apple" /> | Facial recognition systems are computer programs that match faces against a database [https://en.wikipedia.org/wiki/Facial_recognition_system]. A trivial task for humans, achieving high levels of accuracy has been difficult for computers until recently.<ref> 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/ </ref> Deep learning [https://en.wikipedia.org/wiki/Deep_learning] through the use of convolutional neural networks [https://en.wikipedia.org/wiki/Convolutional_neural_network] currently dominates the facial recognition field.<ref> Almabdy, S., & Elrefaei, L. (2019). Deep convolutional neural network-based approaches for face recognition. ''Applied Sciences'', 9(20), 4397. https://doi.org/10.3390/app9204397 </ref> However, deep learning uses much more memory, disk storage, and computational resources than traditional computer vision, presenting significant challenges to facial recognition with the limited hardware capabilities of smartphones.<ref name="apple"> 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 </ref> Accordingly, smartphone manufacturers have taken to using processors with dedicated neural engines for deep learning tasks <ref> 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/ </ref> as well as creating simpler and more compact models that mimic the behavior of more complex models.<ref name="apple" /> | ||
== Model == | == Model == | ||
| − | + | Facial recognition systems accomplish their tasks by detecting the presence of a face, analyzing its features, and confirming the identity of the person.<ref> Klosowski, T. (2020, July 15). ''Facial recognition is everywhere. here's what we can do about it.'' The New York Times. Retrieved November 14, 2022, from https://www.nytimes.com/wirecutter/blog/how-facial-recognition-works/ </ref> Training data is fed into a facial detection algorithm. The two most popular such methods are the Viola-Jones algorithm and the use of convolutional neural networks <ref name="enriquez"> Enriquez, K. (2018, May 15). (thesis). ''Faster face detection using Convolutional Neural Networks & the Viola-Jones algorithm.'' California State University Stanislaus. Retrieved November 14, 2022, from https://www.csustan.edu/sites/default/files/groups/University%20Honors%20Program/Journals/01_enriquez.pdf. </ref> | |
== History == | == History == | ||
Revision as of 23:32, 21 October 2022
By Kenneth Wu
Note: This page is incomplete.
Facial recognition systems are computer programs that match faces against a database [1]. A trivial task for humans, achieving high levels of accuracy has been difficult for computers until recently.[1] Deep learning [2] through the use of convolutional neural networks [3] currently dominates the facial recognition field.[2] However, deep learning uses much more memory, disk storage, and computational resources than traditional computer vision, presenting significant challenges to facial recognition with the limited hardware capabilities of smartphones.[3] Accordingly, smartphone manufacturers have taken to using processors with dedicated neural engines for deep learning tasks [4] as well as creating simpler and more compact models that mimic the behavior of more complex models.[3]
Contents
Model
Facial recognition systems accomplish their tasks by detecting the presence of a face, analyzing its features, and confirming the identity of the person.[5] Training data is fed into a facial detection algorithm. The two most popular such methods are the Viola-Jones algorithm and the use of convolutional neural networks [6]
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/
- ↑ Almabdy, S., & Elrefaei, L. (2019). Deep convolutional neural network-based approaches for face recognition. Applied Sciences, 9(20), 4397. https://doi.org/10.3390/app9204397
- ↑ 3.0 3.1 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/
- ↑ Klosowski, T. (2020, July 15). Facial recognition is everywhere. here's what we can do about it. The New York Times. Retrieved November 14, 2022, from https://www.nytimes.com/wirecutter/blog/how-facial-recognition-works/
- ↑ Enriquez, K. (2018, May 15). (thesis). Faster face detection using Convolutional Neural Networks & the Viola-Jones algorithm. California State University Stanislaus. Retrieved November 14, 2022, from https://www.csustan.edu/sites/default/files/groups/University%20Honors%20Program/Journals/01_enriquez.pdf.
