A Systematic Review of Fingerprint Recognition System Development

Appati, Justice Kwame; Nartey, Prince Kofi; Yaokumah, Winfred; Abdulai, Jamal-Deen

doi:10.4018/ijssci.300358

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Ear recognition is a biometric technology that has emerged in recent years since ears, similar to fingerprints [1], irises [2], and faces [3], contain many specific and unique features [4] that can be used to identify a person [5]. The process of ear image acquisition is not dependent on the subject's cooperation and is non-invasive and non-contact.…”

Section: Introductionmentioning

confidence: 99%

“…Our contributions can be summarized as follows: (1) We propose the accommodative DropBlock (AccDrop), which has advantages over DropBlock [18] in dealing with unconstrained ear databases. It can generate drop masks with adaptive shapes, which effectively improves the generally poor recognition performance of existing ear recognition systems when facing unconstrained ear databases in realistic scenarios.…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…They used questionnaires and make statistical analysis, mainly accomplished by using the SPSS software and lack of novel design in methodologies. There are relatively few studies concerning on data mining by investigating machine learning methods for qualitative or quantitative analysis [ 12 , 13 ]. A review article discussed the possibility of artificial intelligence technologies be analyzed for enhancing the educational systems for semantic web-based education [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of college students’ canteen consumption by broad learning clustering: A case study in Guangdong Province, China

et al. 2022

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Investigation on college students’ consumption ability help classify them as from rich or relative poor family, thus to distinguish the students who are in urgent need for government’s economic support. As canteen consumption is the main part of the expenses of the college students, we proposed the adjusted K-means clustering methods for discrimination of the college students at different economic levels. To improve the discrimination accuracy, a broad learning network architecture was built up for extracting informative features from the students’ canteen consumption records. A fuzzy transformed technique was combined in the network architecture to extend the candidate range for identifying implicit informative variables from the single type of consumption data. Then, the broad learning network model is fully trained. We specially designed to train the network parameters in an iterative tuning mode, in order to find the precise properties that reflect the consumption characteristics. The selected feature variables are further delivered to establish the adjusted K-means clustering model. For the case study, the framework of combining the broad learning network with the adjusted K-means method was applied for the discrimination of the canteen consumption data of the college students in Guangdong province, China. Results show that the most optimal broad learning architecture is structured with 14 hidden nodes, the model training and testing results are appreciating. The results indicated that the framework was feasible to classify the students into different economic levels by analyzing their canteen consumption data, so that we are able to distinguish the students who are in need for financial aid.

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A Hybrid Approach for Protection Against Rumours in a IoT Enabled Smart City Environment

Mishra

Hsu²,

Arya³

et al. 2023

Lecture Notes in Networks and Systems

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A Systematic Review of Fingerprint Recognition System Development

Cited by 8 publications

References 36 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

Analysis of college students’ canteen consumption by broad learning clustering: A case study in Guangdong Province, China

A Hybrid Approach for Protection Against Rumours in a IoT Enabled Smart City Environment

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