Difference between revisions of "Smartphone Facial Recognition"

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., &amp; 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" />