A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging andpromising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.

Driver fatigue detection based on eye tracking and dynamk, template matching

This chapter describes an automated technique for detecting the eighteen most important facial feature points using a statistically developed anthropometric face model. Most of the important facial feature points are located just about the area of mouth, nose, eyes and eyebrows. After carefully observing the structural symmetry of human face and performing necessary anthropometric measurements, we have been able to construct a model that can be used in isolating the above mentioned facial feature regions. In the proposed model, distance between the two eye centers serves as the principal parameter of measurement for locating the centers of other facial feature regions. Hence, our method works by detecting the two eye centers in every possible situation of eyes and isolating each of the facial feature regions using the proposed anthropometric face model . Combinations of differnt image processing techniques are then applied within the localized regions for detecting the eighteen most important facial feature points. Experimental result shows that the developed system can detect the eighteen feature points successfully in 90.44% cases when applied over the test databases.

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Detection of Facial Feature Points Using Anthropometric Face Model

In this paper, we present a novel method for automatic 3D face authentication. We introduce a coupled 2D and 3D feature-extraction method to determine the positions of eye sockets. The nose tip is considered as the extreme vertex along the normal directions of eye sockets. Once the nose tip and eye sockets are found, the bilateral symmetrical plane will be determined. The central profile which is on the bilateral symmetrical plane is the foundation for recognizing human face in our method. We use a weighting function for ICP according to the bilateral symmetrical behavior. We take 2.5D range image and its corresponding texture as the input data and compare the scanned model with the specified database model. The value of the weighted distance between two compared models is used for authentication. We have successively implemented this method for the authentication of the human faces. The result illustrates that this method work well in self authentication.

3D face authentication by mutual coupled 3D and 2D feature extraction

The development of man-machine interfaces to control devices with the eyes could be of great impact in handicapped individuals. In this paper a non-obstructive interface is proposed to detect and track iris position based on digital image processing techniques, templates and a reference point mounted near the eye. The position of the iris is detected in four steps: reference detection, iris center detection, iris position computation relative to the reference and determination of the eye position within the virtual keyboard. The position of the iris is projected over a virtual keyboard and it is determined the maximum number of keys that the system is able to discriminate. The percentage of iris correct detection in four different video sequences of 803, 710, 913, 849 frames, respectively were above 98.2%. The reference detection was 100% correct. A virtual keyboard was built allowing 12 horizontal and 8 vertical keys.

Design of a virtual keyboard based on iris tracking

Facial feature detection plays an important role in applications such as human computer interaction, video surveillance, face detection and face recognition. We propose a facial feature detection algorithm for all types of face images in the presence of several image conditions. There are two main step: the facial feature extraction from original face image, and the coverage of the features by rectangular blocks. A neural visual model (NVM) is used to recognize all possibilities of facial feature positions for the first step. Input parameters are obtained from the face characteristics and the positions of facial features not including any intensity information. For the better results, some incorrect decisions of facial feature positions are improved by image processing technique called dilation. Our algorithm is successfully tested with various types of faces which are color images, gray images, binary images, wearing the sunglasses, wearing the scarf, lighting effect, noise and blurring images, color and sketch images from animated cartoon.

Locating essential facial features using neural visual model

In a face recognition system, the normalization of the faces under scale, rotation and translation, as well as under contrast and brightness variation, is a crucial step for the extraction of stable features used to recognize a face. A way to improve the speed and the recognition performance of the overall system, is to have an accurate normalization of the images, especially in scale. Here, we present an algorithm, which constitutes the fine analysis part of a multiscale face detection algorithm in a complex background, and which detects, with a subpixellic precision, the center and the ray of the eyeballs of a person's eyes. The segment joining these two points is then used to achieve a geometrical normalization of the face's image under scale, rotation and translation. One of the major advantages of this method is to reduce greatly the number of possible scales used during the face recognition process.

R. Mariani

Papers

Subpixellic eyes detection