  This paper proposes a fast eye detection method that is based on a Siamese
network for near infrared (NIR) partial face images. NIR partial face images do
not include the whole face of a subject since they are captured using iris
recognition systems with the constraint of frame rate and resolution. The iris
recognition systems such as the iris on the move (IOTM) system require fast and
accurate eye detection as a pre-process. Our goal is to design eye detection
with high speed, high discrimination performance between left and right eyes,
and high positional accuracy of eye center. Our method adopts a Siamese network
and coarse to fine position estimation with a fast lightweight CNN backbone.
The network outputs features of images and the similarity map indicating coarse
position of an eye. A regression on a portion of a feature with high similarity
refines the coarse position of the eye to obtain the fine position with high
accuracy. We demonstrate the effectiveness of the proposed method by comparing
it with conventional methods, including SOTA, in terms of the positional
accuracy, the discrimination performance, and the processing speed. Our method
achieves superior performance in speed.
