Ear acoustic authentication is a type of biometric authentication that uses the ear canal transfer characteristics that show the acoustic characteristics of the ear canal. In ear acoustic authentication, biological information can be acquired from both ears. However, extant literature on an accuracy improvement method using binaural features is inadequate. In this study we experimentally determine a feature that represents the difference between each user to perform a highly accurate authentication. Feature selection was performed by changing the combination of binaural features, and they were evaluated using the ratio of between-class and within-class variance and equal error ratio (EER). We concluded that a method that concatenates the features of both ears has the highest performance.

Authors: Yasuhara, Masaki; Yano, Shohei; Arakawa, Takayuki; Koshinaka, Takafumi
Affiliations: Nagaoka College, Nagaoka City, Niigata, Japan; NEC Corporation, Tokyo, Japan(See document for exact affiliation information.)
AES Convention: 146 (March 2019) Paper Number: 10160
Publication Date: March 10, 2019
Subject: Machine Learning: Part 1

Click to purchase paper as a non-member or you can login as an AES member to see more options.

No AES members have commented on this paper yet.

Subscribe to this discussion

To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:
Username:
Password:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.