[PDF] from wiley.com Full View PixISegNet: pixel‐level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck

Shreshth Saini

Abstract:

n this paper, we present a new iris ROI segmentation algorithm using a deep convolutional neural network (NN) to
achieve the state-of-the-art segmentation performance on well-known iris image data sets. The authors’ model surpasses the
performance of state-of-the-art Iris DenseNet framework by applying several strategies, including multi-scale/ multi-orientation
training, model training from scratch, and proper hyper-parameterisation of crucial parameters. The proposed PixISegNet
consists of an autoencoder which primarily uses long and short skip connections and a stacked hourglass network between
encoder and decoder. There is a continuous scale up–down in stacked hourglass networks, which helps in extracting features at
multiple scales and robustly segments the iris even in an occluded environment. Furthermore, cross-entropy loss and content
loss optimise the proposed model. The content loss considers the high-level features, thus operating at a different scale of
abstraction, which compliments the cross-entropy loss, which considers pixel-to-pixel classification loss. Additionally, they have
checked the robustness of the proposed network by rotating images to certain degrees with a change in the aspect ratio along
with blurring and a change in contrast. Experimental results on the various iris characteristics demonstrate the superiority of the
proposed method over state-of-the-art iris segmentation methods considered in this study. In order to demonstrate the network
generalisation, they deploy a very stringent TOTA (i.e. train-once-test-all) strategy. Their proposed method achieves E1 scores of
0.00672, 0.00916 and 0.00117 on UBIRIS-V2, IIT-D and CASIA V3.0 Interval data sets, respectively. Moreover, such a deep
convolutional NN for segmentation when included in an end-to-end iris recognition system with a siamese based matching
network will augment the performance of the siamese network.