OPTIMIZING HYPERPARAMETERS OF A DEEP LEARNING ALGORITHM FOR A REAL TIME FACE RECOGNITION SYSTEM

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dc.contributor.authorJanet O. Jooda
dc.date.accessioned2025-05-27T11:06:10Z
dc.date.available2025-05-27T11:06:10Z
dc.date.issued2024
dc.description.abstractConvolutional 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.
dc.identifier.citationMayowa 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
dc.identifier.issn1001-0920
dc.identifier.urihttps://repository.run.edu.ng/handle/123456789/4807
dc.language.isoen
dc.publisherKongzhi yu Juece/Control and Decision
dc.relation.ispartofseriesVolume 39, ; Issue 04,
dc.titleOPTIMIZING HYPERPARAMETERS OF A DEEP LEARNING ALGORITHM FOR A REAL TIME FACE RECOGNITION SYSTEM
dc.typeArticle
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