Fingerprint pattern identification and classification approach based on convolutional neural networks

Wu, Fan; Zhu, Jiang; Guo, Xiwen

doi:10.1007/s00521-019-04499-w

Cited by 16 publications

(14 citation statements)

References 27 publications

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“…Wu et al [16] present fingerprint patterns that were classified into six types, and the accuracy of the recognition were improved to facilitate the research on human personality characteristics. Based on this idea, a six-category fingerprint database is annotated manually, and CNN is proposed for identifying real fingerprint patterns.…”

Section: Related Workmentioning

confidence: 99%

A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

et al. 2021

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Modern access controls employ biometrics as a means of authentication to a great extent. For example, biometrics is used as an authentication mechanism implemented on commercial devices such as smartphones and laptops. This paper presents a fingerprint biometric cryptosystem based on the fuzzy commitment scheme and convolutional neural networks. One of its main contributions is a novel approach to automatic discretization of fingerprint texture descriptors, entirely based on a convolutional neural network, and designed to generate fixed-length templates. By converting templates into the binary domain, we developed the biometric cryptosystem that can be used in key-release systems or as a template protection mechanism in fingerprint matching biometric systems. The problem of biometric data variability is marginalized by applying the secure block-level Bose–Chaudhuri–Hocquenghem error correction codes, resistant to statistical-based attacks. The evaluation shows significant performance gains when compared to other texture-based fingerprint matching and biometric cryptosystems.

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Section: Related Workmentioning

confidence: 99%

A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

et al. 2021

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“…output layer of size 7 (representing 7 gait activities). The first layer size (16 being a multiple of 2 and closer to the average of input (18), output (1)) is selected, however, any number between number of input and output can be selected. Also higher accuracy is observed using first layer of size 16 than 8.…”

Section: Deep Learning Based Multi-modality Sensor Fusionmentioning

confidence: 99%

“…Deep Learning (DL) has become the state-of-the-art in many pattern classification techniques such as iris [16], face [17], finger-print [18], palm vein [19], ECG [20], human action [21] and gait [22] etc. DL models require minimal pre-processing on complex data and are capable of achieving robust and improved accuracies when dealing with larger volumes and ranges of datasets.…”

Section: Introductionmentioning

confidence: 99%

Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach

Yunas

Ozanyan

2021

IEEE Sensors J.

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Floor Sensors (FS) are used to capture information from the force induced on the contact surface by feet during gait. On the other hand, the Ambulatory Inertial Sensors (AIS) are used to capture the velocity, acceleration and orientation of the body during different activities. In this paper, fusion of the stated modalities is performed to overcome the challenge of gait classification from wearable sensors on the lower portion of human body not in contact with ground as in FS. Deep learning models are utilized for the automatic feature extraction of the ground reaction force obtained from a set of 116 FS and body movements from AIS attached at 3 different locations of lower body, which is novel. Spatio-temporal information of disproportionate inputs obtained from the two modalities is balanced and fused within deep learning network layers whilst reserving the categorical content for each gait activity. Our approach of fusion compensates the degradation in spatio-temporal accuracies in individual modalities and makes the overall classification outcomes more accurate. Further assessment of multi-modality based results show significant improvements in f-scores using different deep learning models i.e., LSTM (99.90%), 2D-CNN (88.73%), 1D-CNN (94.97%) and ANN (89.33%) respectively.

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“…The components of CNN 10 are shown in Figure 1, including input layer, convolution layer, pooling layer, and output layer. The input layer inputs the image data, and the convolution layer can obtain the global information of the image through the local connection, and then reduce the parameter amount through weight sharing.…”

Section: Damaged Fingerprint Recognition Based On Cnnmentioning

confidence: 99%

Feature extraction, recognition, and matching of damaged fingerprint: Application of deep learning network

2020

Concurrency and Computation

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With the improvement of informatization and the development of computer technology, identity recognition with fingerprint has been the growing trend, but the stained fingerprint will make recognition difficult. To solve the above problem, this paper briefly introduced the traditional point matching fingerprint recognition algorithm and the damaged fingerprint recognition method based on the convolution neural network (CNN). Then in MATLAB software, the damaged fingerprint recognition method based on CNN was simulated and compared with the traditional point matching recognition method and traditional CNN recognition method. The results showed that the improved CNN recognition method iterated fewer times and achieved smaller training error during the training; the improved CNN method had lower false recognition rate and rejection rate in identifying the unknown fingerprint; the traditional point matching method spent the most time in identifying the unknown fingerprint, followed by the traditional CNN method and the improved CNN recognition method. This study used CNN for fingerprint recognition and improved CNN to improve its recognition accuracy for the damaged fingerprint. The improved CNN can effectively enhance the recognition accuracy of the damaged fingerprint, which provides a useful reference for the improvement of the accuracy and applicability of the fingerprint recognition system.

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Fingerprint pattern identification and classification approach based on convolutional neural networks

Cited by 16 publications

References 27 publications

A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

A Novel Fingerprint Biometric Cryptosystem Based on Convolutional Neural Networks

Gait Activity Classification Using Multi-Modality Sensor Fusion: A Deep Learning Approach

Feature extraction, recognition, and matching of damaged fingerprint: Application of deep learning network

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