  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 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. A brief updated literature
review is also provided.
