Multibiometrics Using a Single Face Image
This paper addresses the problem of improving biometric recognition accuracy for individuals by leveraging multiple traits from a single input, which is an incremental improvement for biometric security systems.
This paper proposes a multibiometric method that extracts and combines five biometric traits (face, iris, periocular, nose, eyebrow) from a single face image. This approach aims to improve recognition performance without sacrificing user convenience, and its effectiveness was demonstrated through experiments on the CASIA Iris Distance database.
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.