Fast Eye Detector Using Metric Learning for Iris on The Move
- URL: http://arxiv.org/abs/2202.10671v1
- Date: Tue, 22 Feb 2022 05:02:21 GMT
- Title: Fast Eye Detector Using Metric Learning for Iris on The Move
- Authors: Yuka Ogino, Takahiro Toizumi and Masato Tsukada
- Abstract summary: This paper proposes a fast eye detection method based on fully-convolutional Siamese networks for iris recognition.
We use CosFace as a loss function for training to discriminate the left and right eyes with high accuracy even with a shallow network.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper proposes a fast eye detection method based on fully-convolutional
Siamese networks for iris recognition. The iris on the move system requires to
capture high resolution iris images from a moving subject for iris recognition.
Therefore, capturing images contains both eyes at high-frame-rate increases the
chance of iris imaging. In order to output the authentication result in real
time, the system requires a fast eye detector extracting the left and right eye
regions from the image. Our method extracts features of a partial face image
and a reference eye image using Siamese network frameworks. Similarity heat
maps of both eyes are created by calculating the spatial cosine similarity
between extracted features. Besides, we use CosFace as a loss function for
training to discriminate the left and right eyes with high accuracy even with a
shallow network. Experimental results show that our method trained by CosFace
is fast and accurate compared with conventional generic object detection
methods.
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