A literature survey on robust and efficient eye localization in real-life scenarios <s> Active infrared lighting characteristics <s> Non-intrusive methods based on active remote IR illumination for eye tracking are important for many applications of vision-based man-machine interaction. One problem that has plagued those methods is their sensitivity to lighting condition change. This tends to significantly limit their scope of application. In this paper, we present a new real-time eye detection and tracking methodology that works under variable and realistic lighting conditions. Based on combining the bright-pupil effect resulted from IR light and the conventional appearance-based object recognition technique, our method can robustly track eyes when the pupils are not very bright due to significant external illumination interferences. The appearance model is incorporated in both eyes detection and tracking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera. <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Active infrared lighting characteristics <s> Most eye trackers based on active IR illumination require distinctive bright pupil effect to work well. However, due to a variety of factors such as eye closure, eye occlusion, and external illumination interference, pupils are not bright enough for these methods to work well. This tends to significantly limit their scope of application. In this paper, we present an integrated eye tracker to overcome these limitations. By combining the latest technologies in appearance-based object recognition and tracking with active IR illumination, our eye tracker can robustly track eyes under variable and realistic lighting conditions and under various face orientations. In addition, our integrated eye tracker is able to handle occlusion, glasses, and to simultaneously track multiple people with different distances and poses to the camera. Results from an extensive experiment shows a significant improvement of our technique over existing eye tracking techniques. <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Active infrared lighting characteristics <s> Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups <s> BIB003
One of the most effective ways to deal with the lighting changes is the active near-infrared (NIR) imaging techniques, due to the fact that under the active IR lighting, the pupil and iris will show different illumination properties. In particular, the pupil usually has a larger reflection rate than the iris, resulting in a bright spot at pupil position. This bright spot is a good indicator of the pupil and can be used for eye localization BIB001 BIB002 (cf. Fig. 5 ). In practice, a near-infrared light source with a wavelength from 780 to 880 nm will meet the requirements of most in-door application scenarios. Due to its robustness against visible lighting changes, this method has been widely used in driver fatigue detection and face recognition BIB003 . It should be mentioned, however, that there exist several conditions (restrictions) that must be satisfied to ensure good performance, such as opened eye states and the on-axis light, together with NIR imaging hardware. Li et al. BIB003 observed that although active NIR makes the appearance of the eyes robust to different lighting conditions in general, the glasses and eye states may cause trouble for precise eye localization (cf. Fig. 5 ), and they proposed a tree-structured detector to carefully address this issue BIB003 .