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Accurate and efficient eye gaze estimation is important for emerging consumerelectronic systems such as driver monitoring systems and novel user interfaces.Such systems are required to operate reliably in difficult, unconstrainedenvironments with low power consumption and at minimal cost. In this paper anew hardware friendly, convolutional neural network model with minimalcomputational requirements is introduced and assessed for efficientappearance-based gaze estimation. The model is tested and compared againstexisting appearance based CNN approaches, achieving better eye gaze accuracywith significantly fewer computational requirements. A brief updated literaturereview is also provided.

Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems