模式识别实验室知识库

Iris semantic segmentation in less-constrained scenarios is the basis of new generation of iris recognition technology. In this paper, we reexamined our approach iris segmentation framework, named Seg-Edge bilateral constraint network (SEN), which contains an edge map generating network which passes detailed edge information from low level convolutional layers to iris semantic segmentation analysis layers and segmentation-edge bilateral constraint structure for focusing on interesting objects. To reduce the number of network parameters, we propose pruning filters and corresponding feature maps that are identified as useless by 𝑙1-norm and 𝑙2-norm, which results in a lightweight iris segmentation network while keeping the performance almost intact or even better. A novel 𝑙1-norm or [𝑙1-norm, 𝑙2-norm] clustering based pruning method is proposed to improve pruning effect and avoid the time consuming manual design. Experimental results suggest that the proposed SEN structure outperforms the state-of-the-art iris segmentation methods, and the clustering based pruning methods outperform manual design in both compression ratio and accuracy.