基于轻量化网络的眼部特征分割方法

To deal with the problem of accurate and fast recognition and detection of pupil and iris features with high-resolution eye images, a lightweight semantic segmentation network DIA-UNet (dual input attention UNet) was proposed based on the UNet framework. It was arranged that, a symmetric dual coding structure was adopted to obtain the features of the eye grayscale image and its contour image synchronously, the dual attention mechanism was used to carry out the feature filtering on the decoding end, and taking the deep fusion features as a semantic segmentation output. The test results from the CASIA-Iris-Interval and high-resolution pupil datasets show that, compared with other lightweight semantic segmentation networks, the proposed DIA-UNet can guarantee the accuracy of iris and pupil segmentation, while the number of network parameters is only 0.076 Million and the processing speed is up to 123.5 FPS.