Wahab Abdul, Quek Chai, Tan Chin Keong, Takeda Kazuya
Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore.
IEEE Trans Neural Netw. 2009 Apr;20(4):563-82. doi: 10.1109/TNN.2008.2007906. Epub 2009 Feb 27.
Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.
基于生物识别技术的认证取得的进展使其日益突出，并正被应用于日常任务中。现有的车辆安全系统仅依靠警报或智能卡作为保护形式。一种利用驾驶行为的生物识别驾驶员识别系统是一种极具创新性和个性化的方法，可以纳入现有的车辆安全系统，形成多模态识别系统，并提供更高程度的多层次保护。在本文中，已进行了详细研究以对个体驾驶行为进行建模，以便识别可有效且高效地用于描绘每个驾驶员特征的特征。提出并实现了基于高斯混合模型（GMM）的特征提取技术。从油门和刹车踏板压力中提取的特征随后被用作模糊神经网络（FNN）系统的输入，以确定驾驶员的身份。使用两种模糊神经网络，即进化模糊神经网络（EFuNN）和基于自适应网络的模糊推理系统（ANFIS），来证明所提出的两种特征提取技术的可行性。将这些性能与使用多层感知器（MLP）网络的人工神经网络（NN）实现以及基于GMM的统计方法进行了比较。进行了广泛的测试，结果表明FNN在实时驾驶员识别和验证方面具有巨大潜力。此外，驾驶员行为特征分析在执法以及与公交车和卡车司机打交道的公司中还有许多其他潜在应用。
