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Eye-MMS: Miniature Multi-Scale Segmentation Network of Key Eye-Regions in Embedded Applications

Boutros, Fadi and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan (2019):
Eye-MMS: Miniature Multi-Scale Segmentation Network of Key Eye-Regions in Embedded Applications.
pp. 3665-3670, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 27.-28. Oct. 2019, DOI: 10.1109/ICCVW.2019.00452,
[Conference or Workshop Item]

Abstract

Segmentation of the iris or sclera is an essential processing block in ocular biometric systems. However, humancomputer interaction, as in VR/AR applications, requires multiple region segmentation to enable smoother interaction and eye-tracking. Such application does not only demand highly accurate and generalizable segmentation, it requires such segmentation model to be appropriate for the limited computational power of embedded systems. This puts strict limits on the size of the deployed deep learning models. This work presents a miniature multi-scale segmentation network consisting of inter-connected convolutional modules. We present a baseline multi-scale segmentation network and modify it to reduce its parameters by more than 80 times, while reducing its accuracy by less than 3%, resulting in our Eye-MMS model containing only 80k parameters. This work is developed on the OpenEDS database and is conducted in preparation for the OpenEDS Semantic Segmentation Challenge.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Boutros, Fadi and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan
Title: Eye-MMS: Miniature Multi-Scale Segmentation Network of Key Eye-Regions in Embedded Applications
Language: English
Abstract:

Segmentation of the iris or sclera is an essential processing block in ocular biometric systems. However, humancomputer interaction, as in VR/AR applications, requires multiple region segmentation to enable smoother interaction and eye-tracking. Such application does not only demand highly accurate and generalizable segmentation, it requires such segmentation model to be appropriate for the limited computational power of embedded systems. This puts strict limits on the size of the deployed deep learning models. This work presents a miniature multi-scale segmentation network consisting of inter-connected convolutional modules. We present a baseline multi-scale segmentation network and modify it to reduce its parameters by more than 80 times, while reducing its accuracy by less than 3%, resulting in our Eye-MMS model containing only 80k parameters. This work is developed on the OpenEDS database and is conducted in preparation for the OpenEDS Semantic Segmentation Challenge.

Uncontrolled Keywords: Biometrics Head mounted displays Image segmentation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Event Location: Seoul, Korea (South)
Event Dates: 27.-28. Oct. 2019
Date Deposited: 17 Apr 2020 10:01
DOI: 10.1109/ICCVW.2019.00452
Official URL: https://ieeexplore.ieee.org/document/9022048
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