NDLI: PixISegNet: pixel-level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck

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PixISegNet: pixel-level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck

Content Provider	IET Digital Library
Author	Jha, Ranjeet Ranjan Jaswal, Gaurav Gupta, Divij Saini, Shreshth Nigam, Aditya
Abstract	In 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 E 1 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.
Starting Page	11
Ending Page	24
Page Count	14
ISSN	20474938
Volume Number	9
e-ISSN	20474946
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/9/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2019.0025
Journal	IET Biometrics
Publisher Date	2019-08-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biometric Segmentation Research CASIA V3.0 Interval Data Sets Computer Vision And Image Processing Technique Content Loss Optimisation Convolutional Neural Nets Cross-entropy Loss Decoding Deep Convolutional Neural Network Deep Convolutional NN Encoder–decoder Entropy Code Feature Extraction Hyper-parameterisation IIT-D Interval Data Sets Image And Video Coding Image Classification Image Coding Image Matching Image Recognition Image Restoration Image Segmentation Image Segmentation Performance Iris Image Data Sets Iris Recognition Iris ROI Image Segmentation Algorithm Iris-DenseNet Framework Multiscale-multiorientation Training Neural Computing Technique Nonregular Reflections Pix-SegNet Pixel-level Iris Segmentation Network Pixel-to-pixel Classification Loss Salient Iris Feature Siamese Matching Network Stacked Hourglass Bottleneck Stacked Hourglass Network TOTA Strategy Train-once-test-all Strategy UBIRIS-V2 Interval Data Sets
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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PixISegNet: pixel-level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck