Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems
This work addresses the need for reliable eye-gaze estimation in cost-sensitive applications like driver monitoring, though it is incremental as it builds on existing CNN approaches.
The paper tackled efficient eye-gaze estimation for low-power consumer systems by introducing a hardware-friendly CNN model, achieving better accuracy with significantly fewer computational requirements compared to existing methods.
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.