A Method based on Convolutional Neural Networks for Fingerprint Segmentation

Serafim, Paulo Bruno de Sousa; Medeiros, Aldísio G.; Rego, Paulo A. L.; Maia, José Gilvan Rodrigues; Trinta, Fernando; Maia, Marcio E. F.; Macêdo, José Antônio Fernandes de; Neto, Aloísio Vieira Lira

doi:10.1109/ijcnn.2019.8852236

Cited by 24 publications

(22 citation statements)

References 18 publications

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“…For this purpose clustering with neighboring points. [4] proposed an algorithm which is used to segment region of interest in fingerprint image using convolutional neural networks (CNN) without pre-processing steps. Han [5] proposed fingerprint image enhancement technique termed as adaptive median filter which is used to remove impulse noise.…”

Section: Related Workmentioning

confidence: 99%

Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

Tamrakar*¹,

Gupta²

2020

IJRTE

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A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. One of the main biometric system is fingerprint recognition system. The reduced rate of performance of fingerprint verification system is due to many reasons such as displacement of finger during scanning, moisture on scanner, etc. The result and accuracy of fingerprint recognition depends on the presence of valid minutiae. According to literature several Fingerprint Recognition System are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. This research work is focused on designing of fingerprint verification/classification including feature extraction methods and learning models for proper classification to label different fingerprints. In order to gain above mentioned objectives, FVC2002 dataset is taken for training and testing. In this dataset there are approx. 72 images which are used for testing purpose. In this dataset there are some blur, distorted as well as partial images also which are considered for recognition. Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) is used for recognition of fingerprint. The result analysis shows approx. 3% enhancement over existing work.

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Section: Related Workmentioning

confidence: 99%

Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

Tamrakar*¹,

Gupta²

2020

IJRTE

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“…The gradient-based segmentation method required high image quality, and not every fingerprint could be well segmented, even if the parameters were tuned. Attention-based image classification [9] and semantic-based segmentation architecture [10] have attracted widespread attention. In practical applications, some medical image tasks are solved by using attention mechanisms.…”

Section: Data and Raw Materialsmentioning

confidence: 99%

“…In light of residual attention learning [9] and U-Net [22], we tried to use a combined method to study the segmentation of defect fingerprints. The method used in this paper was constructed by module overlays of multiple attention mechanisms and using residuals.…”

Section: Overall Model Architecturementioning

confidence: 99%

“…In 2018, Dinh-Luan Nguyen, Kai Cao, and Anil K. Jain proposed a fingerprint image segmentation method and a deep network model SegFinNet [8] in the field of latent fingerprints. Serafim, P. B. S., Medeiros, A. G., Rego, P. A., et al, 2019 [9] proposed an ROI segmentation method based on CNN to classify image patches and apply filtering techniques for improving the quality of the ROI.…”

Section: Introductionmentioning

confidence: 99%

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XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net

Wan

et al. 2020

Sensors

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Partially defective fingerprint image (PDFI) with poor performance poses challenges to the automated fingerprint identification system (AFIS). To improve the quality and the performance rate of PDFI, it is essential to use accurate segmentation. Currently, most fingerprint image segmentations use methods with ridge orientation, ridge frequency, coherence, variance, local gradient, etc. This paper proposes a method of XFinger-Net for segmenting PDFIs. Based on U-Net, XFinger-Net inherits its characteristics. The attention gate with fewer parameters is used to replace the cascaded network, which can suppress uncorrelated regions of PDFIs. Moreover, the XFinger-Net implements a pixel-level segmentation and takes non-blocking fingerprint images as an input to preserve the global characteristics of PDFIs. The XFinger-Net can achieve a very good segmentation effect as demonstrated in the self-made fingerprint segmentation test.

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“…Fingerprint segmentation is a challenge problem since it not only needs to be capable of extracting the ridge structure, but also leaves out non-fingerprint noise in the background. In order to promote the precision and fineness of AFIS, many previous arts [2], [3] have investigated a series of tools to improve the efficient of fingerprint segmentation in preprocessing. Based on feature fineness, most fingerprint segmentation algorithms can be arbitrarily formulated into two categories: pixel-wise method [4]- [6] and block-wise method (only block features) [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Dynamic Fingerprint Segmentation Method Based on Fuzzy C-Means and Genetic Algorithm

You

2020

IEEE Access

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In automatic fingerprint identification system (AFIS), fingerprint segmentation plays a crucial role in improving detection accuracy and reducing the computation time of feature extraction. With the goal of refining performance of AFIS, in this paper, we investigate a novel dynamic fingerprint segmentation algorithm. The proposed algorithm is based on the existed dynamic image segmentation algorithm using fuzzy c-means (FCM) and genetic algorithm. Specifically, relying on different gray level of histogram and improved post-processing method, we establish a well-performed fingerprint segmentation system. The extensive results from our empirical experiments demonstrate the high performance of our proposed dynamic fingerprint segmentation algorithm, and its better performance than other competing approaches.

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A Method based on Convolutional Neural Networks for Fingerprint Segmentation

Cited by 24 publications

References 18 publications

Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

Low Resolution Fingerprint Image Verification using CNN Filter and LSTM Classifier

XFinger-Net: Pixel-Wise Segmentation Method for Partially Defective Fingerprint Based on Attention Gates and U-Net

A Novel Dynamic Fingerprint Segmentation Method Based on Fuzzy C-Means and Genetic Algorithm

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