Abstract: Face recognition is one of the key components for future intelligent vehicle applications such as determining whether a person is authorized to operate the vehicle. This study describes the development and implementation of an automatic face recognition system in the car environment. The challenge is to build a fast and accurate system that is able to detect, recognize and verify a driver`s identity with the constraint introduced in the car environment in daylight lighting conditions. A further constraint is to use a low-cost web camera to capture the frontal images. The system consists of two parts. The first is face detection, which is based on combining fast and classical Neural Networks (NN) methods. The second is face recognition and verification, which is based on combining Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. Lighting correction techniques are applied to improve the overall performance. The proposed system has been tested in the car environment and has a recognition rate of 91.4% with 0.75% false acceptance rate. Face detection is achieved in 3.7 sec while face recognition for a car with two persons authorized to operate the vehicle is 1.4 sec.