Smartphone Facial Recognition
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] By Kenneth Wu
Note: This page is incomplete.
Facial recognition systems are computer programs that match faces against a database [4]. A trivial task for humans, achieving high levels of accuracy has been difficult for computers until recently.[5] Deep learning [5] through the use of convolutional neural networks [6] currently dominates the facial recognition field.[6] 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 [7] as well as creating simpler and more compact models that mimic the behavior of more complex models.[3]
Model
Facial recognition systems accomplish their tasks by detecting the presence of a face, analyzing its features, and confirming the identity of the person.[8] Training data is fed into a facial detection algorithm, where the two most popular such methods are the Viola-Jones algorithm and the use of convolutional neural networks.[9] The Viola-Jones algorithm was the first real-time object detection framework, and works by converting images to grayscale and looking for edges that signify the presence of human features. [10] While highly accurate in detecting well-lit front-facing faces and also requiring relatively little memory, it is slower than deep-learning based methods, including the now industry-standard convolutional neural network (CNNs).[9]
Convolutional neural networks are closely related to artificial neural networks (ANNs) [7]. Their architecture is sparse [8], topographic, and feed-forward [9][11] featuring an input and output layer along with three types of hidden layers.[12] Unlike traditional ANNs, CNNs have three dimensions - width, depth, and height - and only connect to a certain subset of the preceding layer.[13] The first hidden layer type is convolutional, which involves using a filter of n x n size with pre-determined values, sweeping across a larger matrix at a pre-determined stride and adding the dot products to an map.[13] This presents a significant advantage over ANNs by greatly reducing the amount of information stored Cite error: Closing </ref> missing for <ref> tag
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 3.2 3.3 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/
- ↑ 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
- ↑ 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/
- ↑ 9.0 9.1 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.
- ↑ Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of Simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. https://doi.org/10.1109/cvpr.2001.990517
- ↑ Gurucharan, M. (2022, July 28). Basic CNN architecture: Explaining 5 layers of Convolutional Neural Network. upGrad. Retrieved November 14, 2022, from https://www.upgrad.com/blog/basic-cnn-architecture/#:~:text=other%20advanced%20tasks.-,What%20is%20the%20architecture%20of%20CNN%3F,the%20main%20responsibility%20for%20computation.
- ↑ Mishra, M. (2020, August 26). Convolutional neural networks, explained. Towards Data Science. Retrieved November 14, 2022, from https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939
- ↑ 13.0 13.1 O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
