A practical method for pupil segmentation in challenging conditions

TL;DR

This paper introduces a low-power, fast pupil segmentation method using approximate computing, suitable for hardware implementation, with negligible error rate and significant improvements in power and speed.

Contribution

A novel pupil segmentation approach leveraging approximate computing to enhance speed and power efficiency for biometric authentication devices.

Findings

Significant reduction in power consumption and processing time.

Negligible error rate demonstrated by PSNR and SSIM metrics.

Suitable for hardware implementation in portable biometric systems.

Abstract

Various methods have been proposed for authentication, including password or pattern drawing, which is clearly visible on personal electronic devices. However, these methods of authentication are more vulnerable, as passwords and cards can be forgotten, lost, or stolen. Therefore, a great curiosity has developed in individual authentication using biometric methods that are based on physical and behavioral features not possible to forget or be stolen. Authentication methods are used widely in portable devices since the lifetime of battery and time response are essential concerns in these devices. Due to the fact that these systems need to be fast and low power, designing efficient methods is still critical. We, in this paper, proposed a new low power and fast method for pupil segmentation based on approximate computing that under trading a minor level of accuracy, significant improvement in power assumption and time saving can be obtained and makes this algorithm suitable for hardware implementation. Furthermore, the experimental results of PSNR and SSIM show that the error rate in this method is negligible.