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]: The localization of eye centers and tracking of gaze constitutes an integral component of many human-computer interaction applications. A number of constraints including intrusiveness, mobility, robustness and high-price of eye tracking systems have hindered the way of the integration of eye trackers in everyday applications. Several 'passive' systems based on a single camera have been lately proposed in the literature, exhibiting though subordinate precision compared to the commercial, hardware-based eye tracking devices. In this paper we introduce an automatic, non-intrusive method for precise eye center localization in low resolution images, acquired from single low-cost cameras. To this end, the proposed system uses color information to derive a novel eye map that emphasizes the iris area and a radial symmetry transform which operates both on the original eye images and the eye map. The performance of the proposed method is extensively evaluated on four publicly available databases containing low resolution images and videos. Experimental results demonstrate great accuracy in challenging cases and resilience to pose and illumination variations, achieving significant improvement over existing methods. Use of color information for accurately localizing eye centers.Statistical analysis of the color distribution of the skin, eye areas and irises.Radial symmetry computation both in grayscale images and in chrominance eye maps.We report high accuracy rates for low resolution color images.

[2]: The estimation of the eye centres is used in several computer vision applications such as face recognition or eye tracking. Especially for the latter, systems that are remote and rely on available light have become very popular and several methods for accurate eye centre localisation have been proposed. Nevertheless, these methods often fail to accurately estimate the eye centres in difficult scenarios, e.g. low resolution, low contrast, or occlusions. We therefore propose an approach for accurate and robust eye centre localisation by using image gradients. We derive a simple objective function, which only consists of dot products. The maximum of this function corresponds to the location where most gradient vectors intersect and thus to the eye’s centre. Although simple, our method is invariant to changes in scale, pose, contrast and variations in illumination. We extensively evaluate our method on the very challenging BioID database for eye centre and iris localisation. Moreover, we compare our method with a wide range of state of the art methods and demonstrate that our method yields a significant improvement regarding both accuracy and robustness.

[3]: We propose a geometrical method, applied over eye-specific features, to improve the accuracy of the art of eye-center localization. Our solution is built upon: (a) checking radially constrained gradient vectors, (b) adding weightage to iris specific features and (c) considering bi-directional image gradients to eliminate errors due to reflection on pupil. Our system outperforms the state of the art methods, when compared collectively across multiple benchmark databases, such as BioID and FERET. Our process is lightweight, robust and significantly fast: achieving 50–60 fps for eye center localization, using a single threaded approach on a 2.4 GHz CPU with no GPU. This makes it practicable for real-life applications.

[4]:
Recently, some authors focused on robust eye center localization without an explicit segmentation of the iris or the pupil. Typically, these are either voting or learning-based approaches. The method of Timm and Barth [2] is a popular voting based approach where pixels cast votes for the eye center based on agreement in the direction of their gradient with the direction of radial rays. A similar voting scheme is suggested by Valenti and Gevers [4] , who also cast votes based on the aforementioned alignment but rely on isophote curvatures in the intensity image to cast votes at the right distance. Skodras and Fakotakis [1] propose a similar method but use color to better distinguish between the eye and the skin. Ahuja al [3] improve the voting using radius constraints, better weights, and contrast normalization.