Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA.
Department of Computer Science, William Paterson University, Wayne, NJ 07470, USA.
Int J Environ Res Public Health. 2022 Mar 6;19(5):3085. doi: 10.3390/ijerph19053085.
Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver assistance systems. These systems can assist various functions for proper driving and estimate drivers' capability of stable driving behavior and road safety. Many studies have proved that the driver's emotions are the significant factors that manage the driver's behavior, leading to severe vehicle collisions. Therefore, continuous monitoring of drivers' emotions can help predict their behavior to avoid accidents. A novel hybrid network architecture using a deep neural network and support vector machine has been developed to predict between six and seven driver's emotions in different poses, occlusions, and illumination conditions to achieve this goal. To determine the emotions, a fusion of Gabor and LBP features has been utilized to find the features and been classified using a support vector machine classifier combined with a convolutional neural network. Our proposed model achieved better performance accuracy of 84.41%, 95.05%, 98.57%, and 98.64% for FER 2013, CK+, KDEF, and KMU-FED datasets, respectively.
机器和深度学习技术是人工智能的两个分支，已被证明在解决高级人类问题方面非常有效。汽车行业目前正在使用这项技术为驾驶员提供先进的驾驶员辅助系统。这些系统可以协助各种功能以实现正确的驾驶，并估计驾驶员稳定驾驶行为和道路安全的能力。许多研究已经证明，驾驶员的情绪是管理驾驶员行为的重要因素，这可能导致严重的车辆碰撞。因此，持续监测驾驶员的情绪有助于预测他们的行为以避免事故。为了实现这一目标，已经开发了一种使用深度神经网络和支持向量机的混合网络架构来预测在不同姿势、遮挡和光照条件下的六个到七个驾驶员的情绪。为了确定情绪，使用 Gabor 和 LBP 特征的融合来找到特征，并使用支持向量机分类器与卷积神经网络相结合进行分类。我们提出的模型在 FER2013、CK+、KDEF 和 KMU-FED 数据集上的 FER 分别达到了 84.41%、95.05%、98.57%和 98.64%的更好性能精度。
