OPTIMIZING HYPERPARAMETERS OF A DEEP LEARNING ALGORITHM FOR A REAL TIME FACE RECOGNITION SYSTEM
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Date
2024
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Kongzhi yu Juece/Control and Decision
Abstract
Convolutional Neural Networks (CNNs) have demonstrated remarkable success in various
image recognition and classification tasks. This model cannot perform well in challenging conditions, such
as, low light, varying camera angles and occlusions or crowded scenes. Therefore, this research Developed
Spider wasp Optimized Convolutional neural network base on real time face recognition system (SWO-
CNN). The outcomes of this research thus lend credence to the assertion that the SWO method, when
combined with CNN technology, enhanced the detection of faces while also accelerating improved. This
research has contributed to knowledge with an empirical proof that the application of SWO based CNN
technique achieved an improved performance with low processing time in the detection of faces in videos
processing. With the developed technique, face detection in surveillance systems could be greatly improved.
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Citation
Mayowa O. OYEDIRAN, Olufemi S. OJO, Adeyinka M. AMOLE, Olubunmi J. JOODA, Olufikayo A. ADEDAPO (2024). Optimizing Hyperparameters Of A Deep Learning Algorithm For A Real Time Face Recognition System, Kongzhi yu Juece/Control and Decision (KZYJC), Volume 39, Issue 04, Pp. 2653 – 2664