Research On Methods And Applications Of Eye Detection And Tracking

The eye is not only the most important sensory organ of human body but also the most salient feature of human face. Research on the eye and its movement is essential for eye based human-computer interaction and understanding human visual mechanism, emotion and behavior. Eye detection and tracking based on computer vision have become dominant methods due to their harmlessness and high accuracy.Eye detection and tracking are the necessary steps of face recognition, facial expression recognition, eye movement analysis and iris recognition. Research of eye detection and tracking involve image processing, computer vision pattern recognition, cognitive psychology science and so on. Research results of eye detection and tracking have widly been used in the fields of industrial inspection, intelligent robots, human computer interaction, public safety, intelligent transportation, psychology, medical diagnostics and military reconaissance.Locating eye center precisely is difficult for existing eye detection and tracking algorithms based on low-quality images. In addition, accurate iris localization is usually carried out on high-quality and high-resolution eye images. Precise eye tracking device is usually very expensive and inconvenient, so its application field is very narrow. To solve these problems, the characteristics of the low quality eye images are studied, and a series of eye detection and tracking algorithms on the basis of previous research results are proposed. In addition, we evaluate the performances of our methods, which have been used in activity recognition and eye gaze estimation, by designing low-cost eye movement recorders.The major innovative researches in this paper are shown as follows:1. To overcome the weakness that eyebrow is difficult to be excluded in the eye windows by traditional methods, a Scale-invariant Gradient Integral Projection Function algorithm is proposed to segment eye region. The features of low-quality eye images are taken into full account in this method. Like other projection methods, its computational complexity is very low. Experiments on images with different scales, poses, and illumination condition demonstrate that our method can effectively remove the interference of the eyebrows with high detection rate.2. In order to improve the accuracy of eye detection in low quality face images, an eye detection method based on gradient integral projection and expectation maximization algorithm is proposed. More precise eye windows are abtained by use of expectation maximization algorithm. Then, the weighted barycenter algorithm is used to improve the detection accuracy. Comparison experimental results on our image database and YaleB face database demonstrate that this method is robust to illumination, head poses, glasses and blinks, and more accurate than four existing projection methods.3. In low-quality images, precise iris center is difficult to be abtained by existing iris localization algorithms, because accurate iris localization methods need high-quality and high resolution eye images. Considering this problem, a novel rectangular integro-variance operator is designed and used to precisely locate both of the irises. Experimental results on FERET and YaleB face database demonstrate that this method is robust to eyelid occlusion, different scale, mild head rotation and illumination changes. The detection rate is over90%under a harsh evaluation criterion, which is far higher than the detction rate of the classical iris localization methods.4. To acquire the eye movement data in low quality videos, a difference image based importance sampling iris tracking method is proposed, and a blink detection method is proposed based on the gradient integral projection algorithm to aviod drift. After denoising by wavelet, eye movement signals abtained by this method have a good performance in activity recognition based on eye movement analysis.5. Eye movement analysis for activity recognition under one Web camera is first proposed. A Web camera is seted on the computer monitor to record the activity videos of reading electronic document, browsing the web and watching video. First, ten novel features are extracted from the eye movement signals. Then, support vector machine is used to activity classification. Finally, we design three experiments based on different application situations, and the expermental results show that the eye movement analysis in common video for activity recognition is a promise sensing method. 6. To extand the application range of the eye tracking, a low-cost and less intrusive eye movement recording system is designed, which is composed of two common CMOS cameras. A segmented weighting-annular Hough transform localization algorithm is proposed on the basis of the eye images captured by this system. Then, a convenient calibration procedure is designed, and the gaze direction is estimated by support vector regression. The experiments demonstrate that the low-cost eye tracker can be used for everyday human computer interaction.