Facial Feature- Based Drowsiness Detection With Multi-Scale Convolutional Neural Network

Recently, the upsurge in accidents is caused due to driver drowsiness arises lack of sleep, fatigue and other health factors. The leads mortality, loss properties serious conditions. Hence, it necessary prevent by drivers. At present, automated model effective for detection recognition. In this research paper, developed a MCNN (Multi-Scale Convolutional Neural Network) framework classification drowsiness. Initially, YAWDD dataset NTHU-DDD utilized acquiring video sequences about driving. acquired converted into frames keyframe extraction selection. With Dlib library face recognition localization facial points extracted frames. image are pre-processed with Cross Guided Bilateral Filtering followed feature hybrid dual-tree complex wavelet transforms Walsh-Hadamard transform vector frame blocks. optimized Flamingo search algorithm (FSA) integrated deep learning Multiscale convolutional neural network (MCNN). proposed method, based FSA drowsy non-drowsy classified. simulation results illustrated attains an accuracy value around 98.38% exhibits 98.26%. performance approximately 6% higher than conventional state-of-art methods.