Deep fingerprint classification network

Ibrahim, Abdulsattar M.; Eesee, Abdulrahman K.; Al-Nima, Raıd Rafı Omar

doi:10.12928/telkomnika.v19i3.18771

Cited by 8 publications

(8 citation statements)

References 38 publications

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“…Figure 4 shows the results of this image when it rotates about 33-degree, 69-degree and 106-degree, saved in DB0, DB1, and DB2 sequentially. When compute F in (12) In the state of image Person18 in Figure 5, and Table 2 shows the identification result, it's clear that Person14's rate features of the input image is equal to 79.965 and it has been rejected, like images of the fact that the comparison rate was less than 80% the requirement proposed in the discrimination step 4 from Algorithm 2. Noticed that the features of this input image is approaching the features of a single image stored in the database DB0 named Person10 more than others by 99.422 ratio, which was rotated at an angle of 35 degrees on the original image, while the ratio of the next it goes back to the forms Person03 and stored in the database DB1 which been rotated 68 degrees angle for original image and the rate features of the input image is equal to 84.421, according of the condition in Algorithm 2, the result have more than one fingerprint image features bigger of 80%, so the maximum value is the nearest features back in to unknown person fingerprint image that for Person10.…”

Section: Resultsmentioning

confidence: 99%

“…Fingerprint identification/recognition has very good scales of the entire desired characteristic like authentication, and every human being possesses fingerprints with the exception of any finger patterns-related failure [2], [13], [33], [35]. Fingerprints are very special, and the details of the fingerprint are imperishable, even if they may tentatively change by little cuts and bruise on the skin or weather effects [9], [12], [35]- [41]. It is typically used in security systems and is compared to other biometrics such as face recognition systems [42]- [62].…”

Section: Introductionmentioning

confidence: 99%

“…Many works dealing with fingerprint verification task is present in recent years [2], [3], [6]- [8], [10]- [12], [15], [18], [20], [28], [29], [34], [37]- [39], [41], [44]- [47], [57], [58], [63]- [89]. Kommini et al [63] and Sharma et al [72] presented a method of fingerprint recognition based on fractal features extracted of the separated image.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

User identification system for inked fingerprint pattern based on central moments

Baker

Alazawi

Ahmed

et al. 2021

IJEECS

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The <span>use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.</span>

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

User identification system for inked fingerprint pattern based on central moments

Baker

Alazawi

Ahmed

et al. 2021

IJEECS

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“…Their experimental results showed that the EfficientNet-B3 model achieved the highest accuracy by comparing those advanced models. Ibrahim et al (2021) proposed an efficient DFCN (deep fingerprint classification network) model to accurately classify real and fake fingerprints. The proposed DFCN achieved high classification performance, where fingerprint images are successfully classified into two categories.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Fingerprint classification combined with Gabor filter and convolutional neural network

He¹,

Machica²,

Arroyo³

et al. 2023

Int. j. adv. appl. sci.

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A fingerprint is an impression left by the friction ridges of a human finger. A fingerprint classification system groups fingerprint according to their characteristics and therefore helps to match a fingerprint against an extensive database of fingerprints. The Henry classification system is widely used among fingerprint classification systems. Some researchers have used traditional machine learning or deep learning for fingerprint classification. Nevertheless, traditional algorithms cannot extract the depth features of the fingerprint, and most deep learning algorithms lack fingerprint image enhancement. So, this paper combined the Gabor Filter and Convolutional Neural Network to extract fingerprint features. The model has two channels, one is a Deep Convolutional Neural Network (DCNN), and the other is a Shallow Convolutional Neural Network (SCNN). The DCNN consists of a neural network with eight layers, which can extract deep features of the fingerprint. The SCNN consists of Gabor Filter and a neural network with two layers that can extract features from clear fingerprint images. This paper uses NIST Special Database 4 for experiments. Experimental results show that the model proposed in this paper has achieved 91.4% accuracy. Compared with other algorithms, this model has higher accuracy than others. It shows that combined with the Gabor Filter and Convolutional Neural Network can better extract the ridge features of fingerprint images.

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“…У роботі [6] показано алгоритм класифікації відбитків пальців. Відбитки пальців класифікуються на п'ять категорій: арка, тентована арка, ліва петля, права петля та виток.…”

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Development of Biometric Identification Methods Based on New Filtration Methods

Nazarkevych¹

2021

UJIT

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The article is devoted to the development of biometric identification methods based on new filtration methods. Biometric identification systems need constant improvement, because they often work slowly and give the wrong result. To increase the reliability of biometric image recognition, the method is formed, which is formed from the stages: segmentation, normalization, local orientation estimation, local estimation, spine frequency estimation, Gabor filter implementation, binarization, thinning. A new filtering method is proposed, which is based on a new type of function – Ateb-functions, which are used next to the Gabor filter. The local orientation can be calculated from local gradients using the arctangent function. The normalization process is performed to evenly redistribute the values of image intensity. When segmenting, the foreground areas in the image are separated from the background areas. A new method of wavelet conversion of biometric image filtering based on Ateb-Gabor has been developed. The Gabor filter is used for linear filtering and improves the quality of the converted image. Symmetry and wavelet transform operations are also used to reduce the number of required multiplication and addition operations. The method is based on the well-known Gabor filter and allows you to rearrange the image with clearer contours. Therefore, this method is applicable to biometric images, where the creation of clear contours is particularly relevant. When Gabor filtering, the image is reconstructed by multiplying the harmonic function by the Gaussian function. Ateb functions are a generalization of elementary trigonometry, and, accordingly, have greater functionality. Ateb-Gabor filtering allows you to change the intensity of the whole image, as well as the intensity in certain ranges, and thus make certain areas of the image more contrasting. Filtering with Ateb functions allows you to change the image from two rational parameters. This allows you to more flexibly manage filtering and choose the best options. When you perform a thinning, the foreground pixels are erased until there is one pixel wide. A standard thinning algorithm is used, or the thinning developed by the authors in other studies. This filtering will provide more accurate characteristics, as it allows you to get more sloping shapes and allows you to organize a wider range of curves. Numerous experimental studies indicate the effectiveness of the proposed method.

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Deep fingerprint classification network

Cited by 8 publications

References 38 publications

User identification system for inked fingerprint pattern based on central moments

User identification system for inked fingerprint pattern based on central moments

Fingerprint classification combined with Gabor filter and convolutional neural network

Development of Biometric Identification Methods Based on New Filtration Methods

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