基于注意力机制和空洞卷积的瞳孔定位算法

瞳孔定位在人机交互中起着关键性作用，然而由于反射、眨眼和睫毛遮挡等噪声以及瞳孔位置不居中、运动导致模糊等问题，使得瞳孔中心定位的准确率下降、鲁棒性减弱，从而导致精确地定位瞳孔仍存在巨大困难。为此，本文提出一种基于注意力机制和空洞卷积的瞳孔检测定位算法。该算法以U Net的编码 解码结构为基础，编码部分采用VGG16并引入空洞卷积，以充分地提取特征，解码部分加入Attention Gate，使得模型具有更好的鲁棒性。然后，使用最小二乘法对网络输出的瞳孔分割图进行拟合，最终根据拟合图像获取瞳孔中心坐标。本算法使用ExCuSe公开的24个数据集进行验证，实验表明该算法可以准确的定位瞳孔位置，平均检测率可达到926%。

Pupil location algorithm based on Attention Gate and dilated convolution

Pupil location plays a key role in Human Computer Interaction. However, due to the noise of reflection, blink and eyelash occlusion, as well as the pupils at the edge of image and blurred due to movement, the accuracy and robustness of pupil center location are reduced, which leads to great difficulties in accurate pupil location. Therefore, this paper proposes a pupil detection and location algorithm based on attention mechanism and cavity convolution. The algorithm is based on the coding decoding structure of U Net, VGG16 is used in the coding part and dilated convolution is introduced to fully extract features, Attention Gate is added in the decoding part to make the model more robust. Then, the least square method is used to fit the pupil segmentation map output by the network. Finally, the pupil center coordinates are obtained according to the fitted image. The algorithm is verified by 24 data sets that are publicly available. Experiments show that the algorithm can accurately locate the pupil position, and the average detection rate can reach 92.6%.