论文信息 - Deconvolutional Neural Network for Pupil Detection in Real-World Environments

Deconvolutional Neural Network for Pupil Detection in Real-World Environments

Eyelid identification provides key data that can be used in several application such as controlling gaze-based HMIs (human machine interfaces), the design of new diagnostic tools for brain diseases, improving driver safety, drowsiness detection, research on advertisement, etc. We propose a novel eyetracking algorithm by learning a deep deconvolutional neural network. To train and test our method, we use several data sets with hand-labeled eye images from real-world tasks. Our method outperforms previous eye tracking methods, improving the results of the current state of the art in a 19%.

Francisco Javier Vera-Olmos | Norberto Malpica

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