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

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.