Open Access

ARTICLE

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

Ghoroub Talal Bostaji^*, Emad Sami Jaha

Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

* Corresponding Author: Ghoroub Talal Bostaji. Email:

Computer Systems Science and Engineering 2023, 47(2), 1571-1591. https://doi.org/10.32604/csse.2023.039701

Received 12 February 2023; Accepted 24 April 2023; Issue published 28 July 2023

Abstract

Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues. Therefore, biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics. Among different viable biometric modalities, the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems. In recent years, most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results. Nevertheless, such traditional models can be sensitive to several unconstrained environmental factors. As such, some traits may be difficult to extract automatically but can still be semantically perceived as soft biometrics. This research proposes a new group of semantic features to be used as soft ear biometrics, mainly inspired by conventional descriptive traits used naturally by humans when identifying or describing each other. Hence, the research study is focused on the fusion of the soft ear biometric traits with traditional (hard) ear biometric features to investigate their validity and efficacy in augmenting human identification performance. The proposed framework has two subsystems: first, a computer vision-based subsystem, extracting traditional (hard) ear biometric traits using principal component analysis (PCA) and local binary patterns (LBP), and second, a crowdsourcing-based subsystem, deriving semantic (soft) ear biometric traits. Several feature-level fusion experiments were conducted using the AMI database to evaluate the proposed algorithm’s performance. The obtained results for both identification and verification showed that the proposed soft ear biometric information significantly improved the recognition performance of traditional ear biometrics, reaching up to 12% for LBP and 5% for PCA descriptors; when fusing all three capacities PCA, LBP, and soft traits using k-nearest neighbors (KNN) classifier.

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Keywords

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