Eye Semantic Segmentation with a Lightweight Model
arXiv:1911.01049 · doi:10.1109/ACCESS.2020.3010011
Abstract
In this paper, we present a multi-class eye segmentation method that can run the hardware limitations for real-time inference. Our approach includes three major stages: get a grayscale image from the input, segment three distinct eye region with a deep network, and remove incorrect areas with heuristic filters. Our model based on the encoder decoder structure with the key is the depthwise convolution operation to reduce the computation cost. We experiment on OpenEDS, a large scale dataset of eye images captured by a head-mounted display with two synchronized eye facing cameras. We achieved the mean intersection over union (mIoU) of 94.85% with a model of size 0.4 megabytes. The source code are available https://github.com/th2l/Eye_VR_Segmentation
To appear in ICCVW 2019. Pre-trained models and source code are available https://github.com/th2l/Eye_VR_Segmentation
References in corpus (5)
- Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations
- Squeeze-and-Excitation Networks
- RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
- OpenEDS: Open Eye Dataset
- Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation