  In existing biometric authentication methods, the user must perform an
authentication operation such as placing a finger in a scanner or facing a
camera. With ear acoustic authentication, acoustic characteristics of the ear
canal are used as biometric information. Therefore, a person wearing earphones
does not need to perform any authentication operation. In biometric
authentication, it is necessary to minimize the false acceptance rate (FAR) so
that no unauthorized user is misidentified as an authorized user. However, if
the FAR is set low, it increases the false rejection rate (FRR), the rate at
which authorized users are falsely recognized as unauthorized users. It has
been reported that when FAR is 0.1%, the FRR in ear acoustic authentication
reaches as much as 22%. In this study, we propose a method that reduces FRR and
enhances authentication accuracy; it generates new ear canal acoustic
characteristics called between-class (BC) features, which combine the ear canal
acoustic characteristics of authorized and unauthorized users features. The
proposed method uses a support vector machine to learn the BC features as the
data of authorized users, then generates a hyperplane in an area close to that
data. We hypothesize that this would decrease the possibility of misidentifying
an unauthorized user as an authorized user and decrease the FRR when the FAR is
0.1%. To evaluate the performance of the proposed method, BC features were
applied to ear acoustic authentication, and FAR and FRR were calculated. The
FRR with FAR = 0.1% was 7.95% lower than with the previous method, and the
equal error rate -- the error rate when FAR and FRR are equivalent -- decreased
by 0.15%. These results confirmed that the proposed method can make ear
acoustic authentication more convenient while maintaining a high level of
security.
