Drowsiness Detection for Drivers using Hybrid T-Distributed Stochastic Neighbor Embedding with Convolutional Neural Network

Drowsiness Detection (DD) is the procedure of identifying signs of drowsiness in individuals, especially in critical situations like driving, heavy machinery operation, or aircraft piloting. Hybrid Bi-directional Long Short Term Memory with Convolutional Neural Network (HBLSTM-CNN) was proposed for driver's DD. At first, an eye dataset was gathered and subsequently subjected to pre-processing using a shape predictor. The pre-processed images are initially analyzed using a BiLSTM to identify the most prominent characteristics, which are subsequently subjected to additional processing in a CNN to serve the purpose of DD. Nonetheless, as the data's dimensionality grows, BiLSTM demands more computational resources, including memory and processing power. So in this paper, Hybrid t-distributed Stochastic Neighbor Embedding with CNN (Ht-SNE-CNN) is introduced for driver's DD. In Ht-SNE-CNN, t-SNE is utilized for reducing dimensionality because of its inherent simplicity, obviating the requirement to train a sophisticated model. It directly computes a lower-dimensional depiction of the data while emphasizing the preservation of local similarities. This inherent simplicity frequently leads to faster and more direct utilization, particularly when handling high-dimensional data. The reduced data are given as input to CNN for driver's DD. The experimental findings demonstrate that the suggested HtSNE-CNN outperforms HBLSTM-CNN with regard to exactness, precision, and recall for detecting driver drowsiness.