Fast Eye Detector Using Siamese Network for NIR Partial Face Images

TL;DR

This paper introduces a rapid eye detection method using a Siamese network tailored for near-infrared partial face images, crucial for iris recognition systems requiring fast and accurate eye localization.

Contribution

The paper presents a novel lightweight Siamese network approach with coarse-to-fine estimation for high-speed, accurate eye detection in NIR partial face images, outperforming existing methods.

Findings

Achieves superior speed compared to conventional methods.

Provides high positional accuracy of eye centers.

Demonstrates effective discrimination between left and right eyes.

Abstract

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.