Deep and discriminative feature learning for fingerprint classification

Ge, Shishu; Bai, Chaochao; Liu, Yan; Liu, Yonghong; Zhao, Tong

doi:10.1109/compcomm.2017.8322877

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…where F is the set of extracted features, and fi = the feature descriptor inputted for a region in the fingerprint image Extracting discriminative features from preprocessed fingerprint images involves various techniques tailored to each specific feature [19]. There are some methods commonly used to extract, which include ridge orientation, ridge frequency, minutiae points, and texture descriptors (Gabor Filters).…”

Section: Proposed Technology Solutionmentioning

confidence: 99%

See 1 more Smart Citation

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System

Odeh,

Yang,

Nwakanma

et al. 2024

Algorithms

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The Industrial Metaverse provides unparalleled prospects for increasing productivity and efficiency across multiple sectors. As wireless sensor networks play an important role in data collection and transmission within this ecosystem, preserving context privacy becomes critical to protecting sensitive information. This paper investigates the issue of context privacy preservation for user validation via AccesSensor in the Industrial Metaverse and presents a technological method to address it. We explore the need for context privacy, look at existing privacy preservation solutions, and propose novel user validation methods that are customized to the Industrial Metaverse’s access system. This method is evaluated on time-based efficiency, privacy method and bandwidth utilization. Our method performs better as compared to the DPSensor. Our research seeks to provide insights and recommendations for developing strong privacy protection methods in wireless sensor networks that operate within the Industrial Metaverse ecosystem.

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Section: Proposed Technology Solutionmentioning

confidence: 99%

“…The more corresponding minutiae points are discovered and the closer they match, the greater the similarity score. Otherwise, they are deemed mismatched, and access is refused [9,19].…”

mentioning

confidence: 99%

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System

Odeh,

Yang,

Nwakanma

et al. 2024

Algorithms

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show abstract

“…This approach was tested on the NIST-SD4 achieving a recognition rate of 96.5%. In [47,48], the authors proposed Res-FingerNet, a deep CNN to tackle the classification task. Moreover, to reduce the intra-class variance and increase the inter-class variance of the fingerprints, they utilized a center loss in the network training phase so that the learned deep features were more discriminative for fingerprint classification tasks.…”

Section: Neural-and Cnn-based Approachesmentioning

confidence: 99%

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

et al. 2021

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Biometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed.

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“…Using the approach in transfer learning, the networks were fine-tuned on the NIST-DB4 database. It achieved an accuracy of 94.4% using VGG-F and 95.05% when using VGG-S. A study by [81] proposed a CNN based network (Res-FingerNet) for automatic fingerprint classification. The method is robust in extracting more abstract and global features from fingerprint images.…”

Section: Application Of Convolutional Neural Network In Fingerprint Image Analysismentioning

confidence: 99%

“…Fingerprint recognition can be challenging, basically because of large intra-class variations caused by displacement, small inter-class variations, and strong noise in the fingerprint patterns, nonlinear distortion, variable pressure, skin condition, and more. These challenges would continue to be open research problems [3,80,81].…”

Section: Open Challenges and Prospect For Future Researchmentioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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Deep and discriminative feature learning for fingerprint classification

Cited by 7 publications

References 20 publications

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System

Context Privacy Preservation for User Validation by Wireless Sensors in the Industrial Metaverse Access System

Fingerprint Classification Based on Deep Learning Approaches: Experimental Findings and Comparisons

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

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