Design and FPGA Implementation of a Light-Weight Calibration-Friendly Eye Gaze Tracking Algorithm

Eye tracking technique is showing broad application potential in daily life. This work proposes an FPGA-based eye gaze tracking system with a differential convolutional neural network structure, which is naturally compatible with calibration operation. Each newly acquired eye image is fed into the proposed algorithm together with the pre-calibrated eye sample as a reference to obtain the relative gaze direction. The final processing output is generated by combining the results of several reference samples. Our design reports an angular gaze tracking accuracy of 3.28° and 3.05° on MPIIGaze and a self-built dataset, respectively. The entire system is integrated on a Xilinx Ultra 96 single computer board with FPGA for acceleration. The total weight of the board is 123.3 grams. The system achieves a processing speed of 15.2 frames per second.