Abstract
This paper compares practical machine learning-based algorithms of detection and recognition such as Haar cascade classifier and local binary pattern histogram (LBPH) method against GoogleNet, which uses convolutional neural network (CNN) architecture, using transfer learning. From the comparative analyzes and studies, it was elucidated that LBPH and Haar cascade are computationally efficient, but CNN has more accuracy despite its longer computational time.
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References
Statista (2020) Facial recognition global market size 2025. https://www.statista.com/statistics/1153970/worldwide-facial-recognition-revenue/
Viola P, Jones M (2004) Robust real-time object detection. Int J Comput Vision 57(2):137–154
Gopi Krishna M, Srinivasulu A, Basak T (2012) Face detection system on ada boost algorithm using haar classifiers. Int J Modern Eng Res (IJMER) 2(6):3996–4000. www.ijmer.com [online]
Yang Y, Hospedales TM (2015) Deep neural networks for sketch recognition
Sharma N et al (2018) An analysis of convolutional neural networks for image classification. Proc Comput Sci 132:377–384. https://doi.org/10.1016/j.procs.2018.05.198
OpenCV: https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html
Face Recognition: Understanding LBPH Algorithm. Towards Data Science (2017). http://www.towardsdatascience.com/face-recognition-how-lbph-works-90ec258c3d6b
Szegedy C et al (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594
MathWorks, Update parameters using stochastic gradient descent with momentum (SGDM) MATLAB sgdmupdate. https://www.mathworks.com/help/deeplearning/ref/sgdmupdate.html
MathWorks, Transfer learning using pretrained network. https://in.mathworks.com/help/deeplearning/ug/transfer-learning-using-pretrained-network.html
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Thampan, N., Muthukumaraswamy, S.A. (2023). On the Studies and Analyzes of Facial Detection and Recognition Using Machine Learning Algorithms. In: Bhateja, V., Sunitha, K.V.N., Chen, YW., Zhang, YD. (eds) Intelligent System Design. Lecture Notes in Networks and Systems, vol 494. Springer, Singapore. https://doi.org/10.1007/978-981-19-4863-3_2
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DOI: https://doi.org/10.1007/978-981-19-4863-3_2
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