ScoreNet: Deep cascade score level fusion for unconstrained ear recognition

Umit Kacar*, Murvet Kirci

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)

Abstract

Although biometric ear recognition has recently gained a considerable degree of attention, it remains difficult to use currently available ear databases because most of them are constrained. Here, the authors introduce a novel architecture called ScoreNet for unconstrained ear recognition. The ScoreNet architecture combines a modality pool with a fusion learning approach based on deep cascade score-level fusion. Hand-crafted and deep learning methods can be used together under the ScoreNet architecture. The proposed method represents the first automated fusion learning approach and is also compatible with parallel processing. The authors evaluated ScoreNet using the Unconstrained Ear Recognition Challenge Database, which is widely considered to be the most difficult database for evaluating ear recognition developed to date, and found that ScoreNet outperformed all other previously reported methods and achieved state-of-the-art accuracy.

Original languageEnglish
Pages (from-to)109-123
Number of pages15
JournalIET Biometrics
Volume8
Issue number2
DOIs
Publication statusPublished - 1 Mar 2019

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2018

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