Convolutional neural network implementation for eye-gaze estimation on low-quality consumer imaging systems
Lemley, Joseph Kar, Anuradha Drimbarean, Alexandru Corcoran, Peter
Lemley, Joseph
Kar, Anuradha
Drimbarean, Alexandru
Corcoran, Peter
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Publication Date
2019-02-15
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Article
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Lemley, J., Kar, A., Drimbarean, A., & Corcoran, P. (2019). Convolutional Neural Network Implementation for Eye-Gaze Estimation on Low-Quality Consumer Imaging Systems. IEEE Transactions on Consumer Electronics, 65(2), 179-187. doi: 10.1109/TCE.2019.2899869
Abstract
Accurate and efficient eye gaze estimation is important for emerging consumer electronic systems, such as driver monitoring systems and novel user interfaces. Such systems are required to operate reliably in difficult, unconstrained environments with low power consumption and at minimal cost. In this paper, a new hardware friendly, convolutional neural network (CNN) model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation. The model is tested and compared against existing appearance-based CNN approaches, achieving better eye gaze accuracy with significantly fewer computational requirements.
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IEEE
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Attribution-NonCommercial-NoDerivs 3.0 Ireland