An adaptive eye gaze tracker system in the integrated cloud computing and mobile device

This paper proposed an adaptive method for tracking eye gaze in the integrated cloud computing and mobile device environment. The task begins with extracting the eye position and the iris contour base on geometrical features. These local gaze features are calculate and integrated to train a neural network. And the estimated gaze point is outputted from the trained NN (Neural Network) in the cloud computing. A utility function is proposed to decide the functionality is performed in the cloud or mobile device adaptively based on device and network conditions. Besides, our proposed method can improve system performance as well as overcome the problem for limited resource of mobile device.