Multibiometrics Using a Single Face Image

TL;DR

This paper introduces a novel multibiometric system that extracts five different biometric traits from a single face image, enhancing recognition performance without compromising user convenience.

Contribution

It proposes a new method to extract multiple biometric traits from one face image, combining face, iris, periocular, nose, and eyebrow features for improved recognition.

Findings

Effective biometric trait extraction from a single face image

Demonstrated improved recognition performance

Validated with experiments on CASIA Iris Distance database

Abstract

Multibiometrics, which uses multiple biometric traits to improve recognition performance instead of using only one biometric trait to authenticate individuals, has been investigated. Previous studies have combined individually acquired biometric traits or have not fully considered the convenience of the system. Focusing on a single face image, we propose a novel multibiometric method that combines five biometric traits, i.e., face, iris, periocular, nose, eyebrow, that can be extracted from a single face image. The proposed method does not sacrifice the convenience of biometrics since only a single face image is used as input. Through a variety of experiments using the CASIA Iris Distance database, we demonstrate the effectiveness of the proposed multibiometrics method.