You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose a new cascaded regressor for eye center detection. Previous methods start from a face or an eye detector and use either advanced features or powerful regressors for eye center localization, but not both. Instead, we detect the eyes more accurately using an existing facial feature alignment method. We improve the robustness of localization by using both advanced features and powerful regression machinery. Unlike most other methods that do not refine the regression results, we make the localization more accurate by adding a robust circle fitting post-processing step. Finally, using a simple hand-crafted method for eye center localization, we show how to train the cascaded regressor without the need for manually annotated training data. We evaluate our new approach and show that it achieves state-of-the-art performance on the BioID, GI4E, and the TalkingFace datasets. At an average normalized error of e < 0.05, the regressor trained on manually annotated data yields an accuracy of 95.07 (BioID), 99.27 (GI4E), and 95.68 (TalkingFace). The automatically trained regressor is nearly as good, yielding an accuracy of 93.9 (BioID), 99.27 (GI4E), and 95.46 (TalkingFace).

Cited Abstract(s):
[1]: We present a fast method for locating iris features in frontal face images based on the Hough transform. it consists of an initial iris detection step and a tracking step which uses iris features from initialisation for speeding lip computation. The purpose of research was to evaluate the feasibility of the method for tracking at 200 frames per second or higher. Processing speed of the prototypical implementation on a 266 Mhz Pentium II PC is approximately 6 seconds for initial iris detection and about 0.05 seconds for each tracking step. Further speed-up using faster equipment seems feasible. The algorithm was applied to images of subjects taken under normal room lighting conditions. Tests showed robustness with respect to shadowing and partial occlusion of the iris. The localisation error was below two pixels. Accuracy for tracking was within one pixel. A reduction of the number of pixels, which are processed in the tracking step by 90 showed a modest degradation of the results.

[2]: Detection of the eyes in human facial images is important in preprocessing for face recognition, since the results can be used to determine the position, size, and direction of the face. This paper describes a method of detection of both irises from a gray-scale facial image. In this method, first a small region (“blob”) which is likely to be an iris is determined by using a separability filter (developed by Fukui and Yamaguchi). Second, taking all the blobs, the costs of all segments connecting the irises and the inclinations of the segments which are within a predetermined value are computed. This cost can be calculated from the regions surrounding the blobs, by using the number of votes for the maximum-voting circle, the separability of the surrounding small regions, the mean brightness within the iris, and the normalized correlation coefficient between a partial image including a pair of blobs and the template for the eyes. The pair of blobs that minimizes the cost is regarded as the irises. The proposed method has been tested by using all 150 images (excluding images with glasses) from the University of Bern Facial Image Database. The experimental results show that the success rates are between 95.3 (worst) and 97.3 (best). If minor errors are tolerated, the success rates are between 96.7 (worst) and 99.3 (best). © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(5): 29–39, 2005; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002 ecjb.20178

[3]: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b mm sup 2 over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.

[4]: Knowing the users point of gaze has significant potential to enhance current human-computer interfaces, given that eye movements can be used as an indicator of the attentional state of a user. The primary obstacle of integrating eye movements into todays interfaces is the availability of a reliable, low-cost open-source eye-tracking system. Towards making such a system available to interface designers, we have developed a hybrid eye-tracking algorithm that integrates feature-based and model-based approaches and made it available in an open-source package. We refer to this algorithm as "starburst" because of the novel way in which pupil features are detected. This starburst algorithm is more accurate than pure feature-based approaches yet is signi?cantly less time consuming than pure modelbased approaches. The current implementation is tailored to tracking eye movements in infrared video obtained from an inexpensive head-mounted eye-tracking system. A validation study was conducted and showed that the technique can reliably estimate eye position with an accuracy of approximately one degree of visual angle.

[5]:
The majority of eye center localization methods are hand-crafted approaches and can be divided into shape and appearance based methods. In the iris recognition literature there are also many segmentation based approaches, such as methods that employ active contours. An extensive overview is given by Hansen and Li [5] . Shape-based techniques make use of the circular or elliptical nature of the iris and pupil. Early methods attempted to detect irises or pupils directly by fitting circles or ellipses. Many techniques have roots in the iris recognition and are based on the integrodifferential operator [3] . Others, such as Kawaguchi al [2] , use blob detection to extract iris candidates and use Hough transform to fit circles to these blobs. Toennies al [1] also employ generalized Hough transform to detect irises, but assume that every pixel is a potential edge point and cast votes proportional to gradient strength. Li al [4] propose the Startburst algorithm, where rays are iteratively cast from the current pupil center estimate to detect pupil boundaries and RANSAC is used for robust ellipse fitting.