An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver

In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.Comment: 4 pages, 2 figures, edited version of published paper in IEEE ICITE 201

Automatic facial analysis for objective assessment of facial paralysis

Facial Paralysis is a condition causing decreased movement on one side of the face. A quantitative, objective and reliable assessment system would be an invaluable tool for clinicians treating patients with this condition. This paper presents an approach based on the automatic analysis of patient video data. Facial feature localization and facial movement detection methods are discussed. An algorithm is presented to process the optical flow data to obtain the motion features in the relevant facial regions. Three classification methods are applied to provide quantitative evaluations of regional facial nerve function and the overall facial nerve function based on the House-Brackmann Scale. Experiments show the Radial Basis Function (RBF) Neural Network to have superior performance

Real-time 3D Face Recognition using Line Projection and Mesh Sampling

The main contribution of this paper is to present a novel method for automatic 3D face recognition based on sampling a 3D mesh structure in the presence of noise. A structured light method using line projection is employed where a 3D face is reconstructed from a single 2D shot. The process from image acquisition to recognition is described with focus on its real-time operation. Recognition results are presented and it is demonstrated that it can perform recognition in just over one second per subject in continuous operation mode and thus, suitable for real time operation