You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms 
Chapter Title:  E. Hand-held devices 
1. Abstract of Cited Paper (BIB001):  Many of the current gaze-tracking systems require that a subject’s head be stabilized and that the interface be fixed to a table. This article describes a prototype system for tracking gaze on the screen of mobile, handheld devices. The proposed system frees the user and the interface from previous constraints, allowing natural freedom of movement within the operational envelope of the system. The method is software-based, and integrates a commercial eye-tracking device (EyeLink I) with a magnetic positional tracking device (Polhemus FASTRAK). The evaluation of the system shows that it is capable of producing valid data with adequate accuracy. 
2. Abstract of Cited Paper (BIB002):  This paper investigates novel ways to direct computers by eye gaze. Instead of using fixations and dwell times, this work focuses on eye motion, in particular gaze gestures. Gaze gestures are insensitive to accuracy problems and immune against calibration shift. A user study indicates that users are able to perform complex gaze gestures intentionally and investigates which gestures occur unintentionally during normal interaction with the computer. Further experiments show how gaze gestures can be integrated into working with standard desktop applications and controlling media devices. 
3. Abstract of Cited Paper (BIB003):  Handheld mobile devices that have a touch screen are widely used but are awkward to use with one hand. To solve this problem, we propose MobiGaze, which is a user interface that uses one's gaze (gaze interface) to operate a handheld mobile device. By using stereo cameras, the user's line of sight is detected in 3D, enabling the user to interact with a mobile device by means of his/her gaze. We have constructed a prototype system of MobiGaze that consists of two cameras with IR-LED, a Windows-based notebook PC, and iPod touch. Moreover, we have developed several applications for MobiGaze. 
4. Abstract of Cited Paper (BIB004):  As smartphones evolve researchers are studying new techniques to ease the human-mobile interaction. We propose EyePhone, a novel "hand-free" interfacing system capable of driving mobile applications/functions using only the user's eyes movement and actions (e.g., wink). EyePhone tracks the user's eye movement across the phone's display using the camera mounted on the front of the phone; more specifically, machine learning algorithms are used to: i) track the eye and infer its position on the mobile phone display as a user views a particular application; and ii) detect eye blinks that emulate mouse clicks to activate the target application under view. We present a prototype implementation of EyePhone on a Nokia N810, which is capable of tracking the position of the eye on the display, mapping this positions to an application that is activated by a wink. At no time does the user have to physically touch the phone display. 
5. Abstract of Cited Paper (BIB005):  Hand-held portable devices have received only little attention as a platform in the eye tracking community so far. This is mainly due to their -- until recently -- limited sensing capabilities and processing power. In this work-in-progress paper we present the first prototype eye gesture recognition system for portable devices that does not require any additional equipment. The system combines techniques from image processing, computer vision and pattern recognition to detect eye gestures in the video recorded using the built-in front-facing camera. In a five-participant user study we show that our prototype can recognise four different continuous eye gestures in near real-time with an average accuracy of 60% on an Android-based smartphone (17.6% false positives) and 67.3% on a laptop (5.9% false positives). This initial result is promising and underlines the potential of eye tracking and eye-based interaction on portable devices. 
6. Abstract of Cited Paper (BIB006):  This paper describes a non-intrusive eyetracking tool for mobile devices by using images acquired by the front camera of the iPhone and iPod Touch. By tracking and interpreting the user's gaze to the smartphone's screen coordinates the user can interact with the device by using a more natural and spotaneous way. The application uses a Haar classifier based detection module for identifying the eyes in the acquired images and subsequently the CAMSHIFT algorithm to find and track the eyes movement and detect the user's gaze. The performance of the proposed tool was evaluated by testing the system on 16 users and the results shown that in about 79% of the times it was able to detect correctly the users' gaze. 
7. Abstract of Cited Paper (BIB007):  This paper describes the design, implementation, and usability evaluation of a neural network based eye tracking system on an unmodified common tablet and discusses the challenges and implications of neural networks as an eye tracking component on a mobile platform. We objectively and subjectively evaluate the usability and performance tradeoffs of calibration, one of the fundamental components of eye tracking. The described system obtained an average spatial accuracy of 3.95° and an average temporal resolution of 0.65 Hz during trials. Results indicate that an increased neural network training set may be utilized to increase spatial accuracy, at the cost of greater physical effort and fatigue. 
8. Abstract of Cited Paper (BIB008):  In this paper, we propose an eye tracking and gaze estimation system for mobile phone. We integrate an eye detector, ::: cornereye center and iso-center to improve pupil detection. The optical flow information is used for eye tracking. We ::: develop a robust eye tracking system that integrates eye detection and optical-flow based image tracking. In addition, we ::: further incorporate the orientation sensor information from the mobile phone to improve the eye tracking for accurate ::: gaze estimation. We demonstrate the accuracy of the proposed eye tracking and gaze estimation system through ::: experiments on some public video sequences as well as videos acquired directly from mobile phone. 
9. Abstract of Cited Paper (BIB009):  Different research works are studying to integrate new ways of interaction with mobiles devices such as smartphone and tablets to provide a natural and easy mode of communication to people. In this paper we propose a new system to monitoring tablets through head motions and eye-gaze gestures recognition. This system is able to open browser application with simples' motions by the head and the eyes. For the face detection, we use a Viola and Jones technique, and for the eyes detection and tracking we used Haar classifier to the detection part and the template matching for the tracking part. We develop this system on android based tablet. Through the front-facing camera the system capture a real-time streaming video on which we implement the detection and recognition module. We test the system on 5 persons and the experiment results shows that this system is robust and it is invariant for the lightness and the moving state of the user. As known the tablet's resources are limited, in consequence the response time of our system is not satisfied for the real time condition. That is why we apply the cloud computing to solve this problem. 
10. Abstract of Cited Paper (BIB010):  Abstract Reliable identification of intentional visual commands is a major problem in the development of eye-movements based user interfaces. This work suggests that the presence of focal visual fixations is indicative of visual commands. Two experiments are described which assessed the effectiveness of this approach in a simple gaze-control interface. Identification accuracy was shown to match that of the commonly used dwell time method. Using focal fixations led to less visual fatigue and higher speed of work. Perspectives of using focal fixations for identification of visual commands in various kinds of eye-movements based interfaces are discussed. 
11. Abstract of Cited Paper (BIB011):  Despite the widespread use of mobile phones and tablets, hand-held portable devices have only recently been identified as a promising platform for gaze-aware applications. Estimating gaze on portable devices is challenging given their limited computational resources, low quality integrated front-facing RGB cameras, and small screens to which gaze is mapped. In this paper we present EyeTab, a model-based approach for binocular gaze estimation that runs entirely on an unmodified tablet. EyeTab builds on set of established image processing and computer vision algorithms and adapts them for robust and near-realtime gaze estimation. A technical prototype evaluation with eight participants in a normal indoors office setting shows that EyeTab achieves an average gaze estimation accuracy of 6.88° of visual angle at 12 frames per second. 
12. Abstract of Cited Paper (BIB012):  Many approaches have been proposed to create an eye tracker based on visible-spectrum. These efforts provide a possibility to create inexpensive eye tracker capable to operate outdoor. Although the resulted tracking accuracy is acceptable for a visible-spectrum head-mounted eye tracker, there are many limitations of these approaches to create a remote eye tracker. In this study, we propose a high-accuracy remote eye tracker that uses visible-spectrum imaging and several gaze communication interfaces suited to the tracker. The gaze communication interfaces are designed to assist people with motor disability. Our results show that the proposed eye tracker achieved an average accuracy of 0.77° and a frame rate of 28 fps with a personal computer. With a tablet device, the proposed eye tracker achieved an average accuracy of 0.82° and a frame rate of 25 fps. The proposed gaze communication interfaces enable users to type a complete sentence containing eleven Japanese characters in about a minute. 
13. Abstract of Cited Paper (BIB013):  Traditional user authentication methods using passcode or finger movement on smartphones are vulnerable to shoulder surfing attack, smudge attack, and keylogger attack. These attacks are able to infer a passcode based on the information collection of user’s finger movement or tapping input. As an alternative user authentication approach, eye tracking can reduce the risk of suffering those attacks effectively because no hand input is required. However, most existing eye tracking techniques are designed for large screen devices. Many of them depend on special hardware like high resolution eye tracker and special process like calibration, which are not readily available for smartphone users. In this paper, we propose a new eye tracking method for user authentication on a smartphone. It utilizes the smartphone’s front camera to capture a user’s eye movement trajectories which are used as the input of user authentication. No special hardware or calibration process is needed. We develop a prototype and evaluate its effectiveness on an Android smartphone. We recruit a group of volunteers to participate in the user study. Our evaluation results show that the proposed eye tracking technique achieves very high accuracy in user authentication.

Literature Review:
Smartphones and tablets provide a unique paradigm for gaze tracking applications. Gaze tracking on handheld devices is done using the device front camera, one or more IR light sources and various computer vision algorithms. Key approaches for handheld-eye tracking are reported by Bulling BIB006 for tracking 3D gaze location from a video using a face and eye detector followed by edge detection and ellipse fitting to determine the eye limbus boundaries. In Lukander BIB002 a commercial eye tracker is integrated with a magnetic position tracking device to track positions of head and eyes. Komogortsev BIB008 uses a single perceptron neural network that maps the features of the eye region to a position on the screen. In Kikuchi BIB013 eye tracking on a tablet is realized with blob and contour detection for deriving iris contours. Then center and planar homography transform is used to convert the coordinate of iris center to display coordinates. Several works use the Haar classifiers/Viola-Jones technique BIB001 such as Alimi BIB010 which describes a system for android tablets to track head motions and eye gaze gestures from video captured using device front camera. The system is claimed to be robust to user movement and lighting conditions. In Kavasidis BIB007 eye tracking for tablets is implemented using a Haar classifier based eye detection module in conjunction with the CAMSHIFT algorithm. Potential applications of eye gaze in handheld devices are presented in Nagamatsu BIB004 describing a user-interface called Mobigaze that uses gaze to operate a handheld device. The system combines gaze and touch for operating the device interface thereby eliminating the Midas touch problem BIB011 . The Midas touch effect BIB011 refers to the phenomenon in which an eye-gaze-sensitive user interface cannot distinguish between user glances for collecting visual information and those for command input. Thus every user fixation may lead to activation even without the actual user intention. Another multimodal gaze based interaction method is presented by Drewes BIB003 for monitoring applications using dwell-time and gaze gesture. The EyePhone introduced by Miluzzo BIB005 controls the phone functions with only gaze and blinks and is free of user touch. It describes an application (Eyemenu) in which gaze direction is used to selectively access and highlight menu buttons. A gaze based user authentication scheme for smartphones is implemented in Wang BIB014 by making the user eyes to follow a moving target on the device screen. In Lai BIB009 , a 3D image display is combined with gaze estimation to achieve adaptive 3D display on a mobile phone that shows different views to corresponding viewing angles. In Imabuchi BIB013 a gaze based communication interface is implemented for gaze based keyboard input and gaming. Fig.9 . Eye tracking implemented in near real-time on a tablet. The tracking algorithm uses cascade classifiers and shape-based approaches to determine the eye region and centers. Elliptical model-fitting and 3D back-projections are then used to determine the eye optical axes and point-of-gaze BIB012 . Table III presents key information obtained from selected papers based on five gaze user platforms. It can be seen that different research works present their results in widely different formats and describe performance of their system under varying operating conditions.