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Gaze Estimation with Graphics 10.17863/CAM.13838 Wood, Erroll William Robinson, Peter Gaze estimation systems determine where someone is looking. Gaze is used for a wide range of applications including market research, usability studies, and gaze-based interfaces. Traditional equipment uses special hardware. To bring gaze estimation mainstream, researchers are exploring approaches that use commodity hardware alone. My work addresses two outstanding problems in this field: 1) it is hard to collect good ground truth eye images for machine learning, and 2) gaze estimation systems do not generalize well -- once they are trained with images from one scenario, they do not work in another scenario. In this dissertation I address these problems in two different ways: learning-by-synthesis and analysis-by-synthesis. Learning-by-synthesis is the process of training a machine learning system with synthetic data, i.e. data that has been rendered with graphics rather than collected by hand. Analysis-by-synthesis is a computer vision strategy that couples a generative model of image formation (synthesis) with a perceptive model of scene comparison (analysis). The goal is to synthesize an image that best matches an observed image. In this dissertation I present three main contributions. First, I present a new method for training gaze estimation systems that use machine learning: learning-by-synthesis using 3D head scans and photorealistic rendering. Second, I present a new morphable model of the eye region. I show how this model can be used to generate large amounts of varied data for learning-by-synthesis. Third, I present a new method for gaze estimation: analysis-by-synthesis. I demonstrate how analysis-by-synthesis can generalize to different scenarios, estimating gaze in a device- and person- independent manner. University of Cambridge 2017-10-17 Thesis Doctoral Doctor of Philosophy (PhD) en EPSRC Doctoral Training Grant studentship for Erroll Wood (RG71269) https://www.repository.cam.ac.uk/handle/1810/267905 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/3f0e1b9e-963c-4e01-b1ee-b1e3195dc518/download 03398f6d2997af32ebe4155ec6403b30 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/2c64f455-4d38-4f6a-969b-68eddc288e1e/download 87eda9de84448d1f82354d60eee3eb5f https://www.rioxx.net/licenses/all-rights-reserved/ Graphics Computer Vision Eye Tracking Gaze Estimation