An Ear Anti-Spoofing Database with Various Attacks

Khiarak, Jalil Nourmohammadi; Pacut, Andrzej

doi:10.1109/ccst.2018.8585637

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Ref. [40] collected a database of ear presentation attack detection, including three types of fake ear attacks-display attacks, print attacks, and video attacks-and used image quality assessment techniques to extract ear features of interest. Ref.…”

Section: Related Workmentioning

confidence: 99%

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Xu,

Liu,

Liu

et al. 2024

Symmetry

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Ear recognition has made good progress as an emerging biometric technology. However, the recognition performance, generalization ability, and feature robustness of ear recognition systems based on hand-crafted features are relatively poor. With the development of deep learning, these problems have been partly overcome. However, the recognition performance of existing ear recognition systems still needs to be improved when facing unconstrained ear databases in realistic scenarios. Another critical problem is that most systems with ear feature template databases are vulnerable to software attacks that disclose users’ privacy and even bring down the system. This paper proposes a software-attack-proof ear recognition system using deep feature learning and blockchain protection to address the problem that the recognition performance of existing systems is generally poor in the face of unconstrained ear databases in realistic scenarios. First, we propose an accommodative DropBlock (AccDrop) to generate drop masks with adaptive shapes. It has an advantage over DropBlock in coping with unconstrained ear databases. Second, we introduce a simple and parameterless attention module that uses 3D weights to refine the ear features output from the convolutional layer. To protect the security of the ear feature template database and the user’s privacy, we use Merkle tree nodes to store the ear feature templates, ensuring the determinism of the root node in the smart contract. We achieve Rank-1 (R1) recognition accuracies of 83.87% and 96.52% on the AWE and EARVN1.0 ear databases, which outperform most advanced ear recognition systems.

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Section: Related Workmentioning

confidence: 99%

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Xu,

Liu,

Liu

et al. 2024

Symmetry

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show abstract

“…Nevertheless, these models still suffer in unconstrained environments where performance results can be significantly affected by pose, illumination, variation of sensors, and occlusion [9][10][11]. Moreover, imposters can circumvent some of these systems using spoof attacks due to the high dependence on the complex geometrical shape of the ear, which often being obscured by hair, hoodies, tattoos, and similar [12]. Hence, they might sometimes fail to meet the high-security accuracy requirements.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

Talal Bostaji,

Sami Jaha

2023

Computer Systems Science and Engineering

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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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A comprehensive survey and deep learning-based approach for human recognition using ear biometric

2021

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Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development.

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An Ear Anti-Spoofing Database with Various Attacks

Cited by 5 publications

References 18 publications

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Anti-Software Attack Ear Identification System Using Deep Feature Learning and Blockchain Protection

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

A comprehensive survey and deep learning-based approach for human recognition using ear biometric

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