| 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. |
|---|
