Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.This research was funded by Public University of Navarra (Pre-doctoral research grant) and by the Spanish Ministry of Science and Innovation under Contract 'Challenges of Eye Tracking Off-the-Shelf (ChETOS)' with reference: PID2020-118014RB-I0

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Medically Applied Artificial Intelligence:from Bench To Bedside

The intent of this thesis was to develop several medically applied artificial intel-ligence programs, which can be considered either clinical decision support tools or pro-grams which make the development of such tools more feasible. The first two projectsare more basic or bench in focus, while the final project is more translational. The firstprogram involves the creation of a residual neural network to automatically detect thepresence of pericardial effusions in point-of-care echocardiography and currently hasan accuracy of 71%. The second program involves the development of a sub-type ofgenerative adverserial network to create synthetic x-rays of fractures for several pur-poses including data augmentation for the training of a neural network to automat-ically detect fractures. We have already generated high quality synthetic x-rays. Weare currently using structural similarity index measurements and Visual Turing testswith three radiologists in order to further evaluate image quality. The final projectinvolves the development of neural networks for audio and visual analysis of 30 sec-onds of video to diagnose and monitor treatment of depression. Our current root meansquare error (RMSE) is 9.53 for video analysis and 11.6 for audio analysis, which arecurrently second best in the literature and still improving. Clinical pilot studies for thisfinal project are underway. The gathered clinical data will be first-in-class and ordersof magnitude greater than other related datasets and should allow our accuracy to bebest in the literature. We are currently applying for a translational NIH grant based onthis work