Isotropic Self-Supervised Learning for Driver Drowsiness Detection With Attention-Based Multimodal Fusion

Driverdrowsiness is an important cause of traffic accidents. Many studies using computer vision techniques to detect driver drowsiness states, such as slow blinking, yawning, and nodding, have demonstrated excellent potential. Although existing studies have made significant progress, the number of samples in the training corpora is small, which makes it difficult for a model to learn effective drowsiness representations from images or videos. To address this issue, we develop an isotropic self-supervised learning (IsoSSL) approach to learn powerful representations of images without relying on human-provided annotations and propose an IsoSSL-MoCo model by combining IsoSSL with momentum contrast (MoCo). To exploit the complementarity of multimodal data, an attention-based multimodal fusion model is also proposed to fuse features from the eye, mouth, and optical flow of the head. Specifically, we first use the IsoSSL-MoCo model to pretrain the image encoders for the three modalities in other datasets. Then, these encoders are fine-tuned and integrated into the proposed fusion model. The feature vectors generated by the image encoders of the three modalities are fed into the recursive layer to extract temporal information. To capture the importance degrees of the effects of temporal features from the three modalities on drowsiness detection, an attention mechanism is introduced to automatically weigh the feature vectors from the recursive layer to improve detection accuracy. Finally, a vector representation is generated by the attention layer and is used to detect driver drowsiness states. Experimental results based on two challenging datasets show that our method outperforms the baseline methods and the latest existing methods.