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Homography normalization is presented as a novel gaze estimation method for uncalibrated setups. The method applies when head movements are present but without any requirements to camera calibration or geometric calibration. The method is geometrically and empirically demonstrated to be robust to head pose changes and despite being less constrained than cross-ratio methods, it consistently performs favorably by several degrees on both simulated data and data from physical setups. The physical setups include the use of off-the-shelf web cameras with infrared light (night vision) and standard cameras with and without infrared light. The benefits of homography normalization and uncalibrated setups in general are also demonstrated through obtaining gaze estimates (in the visible spectrum) using only the screen reflections on the cornea.

This paper investigates whether it is feasible to interact with the small screen of a smartphone using eye movements only. Two of the most common gaze-based selection strategies, dwell time selections and gaze gestures are compared in a target selection experiment. Finger-strokes and accelerometer-based interaction, i.e. tilting, are also considered. In an experiment with 11 subjects we found gaze interaction to have a lower performance than touch interaction but comparable to the error rate and completion time of accelerometer (i.e. tilt) interaction. Gaze gestures had a lower error rate and were faster than dwell selections by gaze, especially for small targets, suggesting that this method may be the best option for hands-free gaze control of smartphones.

Eye movements can be consciously controlled by humans to the extent of performing sequences of predefined movement patterns, or 'gaze gestures'. Gaze gestures can be tracked noninvasively employing a video-based eye tracking system. Gaze gestures hold the potential to become an emerging input paradigm in the context of human-computer interaction (HCI) as low-cost eye trackers become more ubiquitous. The viability of gaze gestures as an innovative way to control a computer rests on how easily they can be assimilated by potential users and also on the ability of machine learning algorithms to discriminate in real time intentional gaze gestures from typical gaze activity performed during standard interaction with electronic devices. In this work, through a set of experiments and user studies, we evaluate the performance of two different gaze gestures modalities, gliding gaze gestures and saccadic gaze gestures, and their corresponding real-time recognition algorithms, Hierarchical Temporal Memory networks and the Needleman-Wunsch algorithm for sequence alignment. Our results show that a specific combination of gaze gesture modality, namely saccadic gaze gestures, and recognition algorithm, Needleman-Wunsch, allows for reliable usage of intentional gaze gestures to interact with a computer with accuracy rates higher than 95% and completion speeds of around 1.5 to 2.5 seconds per gesture. The optimal gaze gesture modality and recognition algorithm do not interfere with otherwise standard human-computer gaze interaction, generating very few false positives during real time recognition and positive feedback from the users. These encouraging results and the low cost eye tracking equipment used, open up a new HCI paradigm for the fields of accessibility and interaction with smartphones, tablets, projected displays and traditional desktop computers.

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