scholarly journals A Gradient Based Approach for Fingerprint Image Segmentation using Morphological Operators

2018 ◽  
Vol 7 (4) ◽  
pp. 2453
Author(s):  
Reji Joy ◽  
Hemalatha S
Keyword(s):  
Image Segmentation ◽  
Individual Recognition ◽  
Input Image ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Morphological Operations ◽  
Fingerprint Image ◽  
Segmentation Process ◽  
Gradient Based ◽  
Fingerprint Segmentation

The advancement of science and technology has made the reliable individual recognition and identification systems to become very popular. From the various biometric characteristics, fingerprint is one of the popular method because of its easiness and not much effort is required to acquire fingerprint. First step for an Automated Fingerprint Identification System (AFIS) is the segmentation of fingerprint from the acquired image. During fingerprint segmentation process the input image is decomposed into foreground and background areas. The foreground area contains information that are needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false features. So in an AFIS, fingerprint image segmentation plays an important role in carefully separating ridge like part (foreground) from noisy background. Gradient based method is commonly used for segmentation process. Since gradient estimation is erroneous in noisy images, the study proposes a combination of gradient mask and morphological operations to segment fingerprint foreground effectively. The results obtained prove that the new method is suited for fingerprint segmentation.

scholarly journals Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

2019 ◽  
Vol 9 (4) ◽  
pp. 2425 ◽  
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Computing Time ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fingerprint Image ◽  
Local Variance ◽  
Dbscan Clustering ◽  
Matching Process ◽  
Fingerprint Segmentation

<span lang="EN-US">Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.</span>

scholarly journals Fingerprint Image Segmentation using Deep Features and SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 1633-1638
Keyword(s):  
Learning Algorithm ◽  
Classification Problem ◽  
Input Image ◽  
Svm Classifier ◽  
Identification System ◽  
Fingerprint Image ◽  
Wide Range ◽  
Fingerprint Segmentation ◽  
Sparse Autoencoder ◽  
The Given

The task of fingerprint segmentation is the most important step in an automated fingerprint identification system. It is essential to separate the fingerprint foreground with ridge and valley structure from the background, which usually contains unwanted data hindering an accurate feature extraction. In the proposed method, fingerprint segmentation is treated as a classification problem by classifying the given input image into foreground class or background class. Here, we have used an unsupervised learning algorithm by using Stacked Sparse Autoencoder (SSAE) to learn the deep features which can very well distinguish the background region from foreground one. Finally, these deep features are given to the SVM classifier. The experimental results prove that the proposed method meets the state-of-the-art results in a wide range of applications.

scholarly journals Fingerprint Image Segmentation: A Review of State of the Art Techniques

2017 ◽  
pp. 28-39 ◽  
Author(s):  
Krishna Prasad K. ◽  
P. S. Aithal
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Input Image ◽  
Identification System ◽  
Initial Image ◽  
Fingerprint Image ◽  
Level 3 ◽  
Level 1 ◽  
Level 2 ◽  
Art Techniques

In Automatic Fingerprint Identification System (AFIS), pre-processing of the image is a crucial process in deciding the quality and performance of the system. Pre-processing is consists many stages as Segmentation, Enhancement, Binarisation, and Thinning. In this segmentation is one of the steps of pre-processing which differentiate foreground and background region of fingerprint images. Segmentation is the separation of the fingerprint region or extraction of the presence of ridges from the background of the initial image. Segmentation is necessary because it constructs the region of interest from the input image, reduces the processing time, increases the recognition or matching process performance, and reduces the probability of false feature extraction. A 100% accurate segmentation is always very difficult, especially in the very poor quality image or partial image filled with noise such as the presence of latent. Fingerprints are made of Ridge and Valley structure and their features are classified in three levels as Level 1, Level 2, and Level 3. Level 1 Features are singular macro details like ridge pattern and ridge flows. Level 2 is ridge local features like ridge bifurcation and ridge ending or simply minutiae points or ridge orientation. Level 3 is micro details like sweat pores, incipient ridges. This paper provides an overview of the state of the art techniques of fingerprint image segmentation and contribution of other researchers on segmentation. This paper also discusses a different class of segmentation algorithms with its measuring parameters, computational complexity, advantages, limitations, and applications.

FINGERPRINT ENHANCEMENT USING WAVELET TRANSFORM COMBINED WITH GABOR FILTER

2004 ◽  
Vol 18 (08) ◽  
pp. 1391-1406 ◽  
Author(s):  
WEIPENG ZHANG ◽  
YUAN YAN TANG ◽  
XINGE YOU
Keyword(s):  
Wavelet Transform ◽  
Gabor Filter ◽  
Wavelet Coefficient ◽  
Input Image ◽  
Identification System ◽  
Gabor Filtering ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Novel Approach

The performance of automatic fingerprint identification system (AFIS) is heavily determined by the quality of the input image, thus an effective method to enhance the fingerprint image is essential in such a system. In this paper, we combine the filter-based method, which is mostly used nowadays with wavelet transform to achieve a more reliable and effective approach to fingerprint enhancement. This novel approach consists of five main steps, namely: (1) normalization, (2) decomposition, (3) wavelet coefficient adjustment, (4) Gabor filtering, and (5) reconstruction. Using this new method, a more clear fingerprint image can be obtained, which can distinctly improve the accuracy of the minutiae extraction module and finally achieve a better performance of the entire system. Experiments have been conducted in our study and positive experimental results have been received, which show that the proposed combined method is more effective and robust than other existing methods such as the filter-based and direct gray-level approaches.

scholarly journals The Study of Randomized Visual Saliency Detection Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Image Segmentation ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Input Image ◽  
Memory Space ◽  
Segmentation Process ◽  
Visual Saliency Detection

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

Research of Automatic Fingerprint Recognition Essential Algorithms

2011 ◽  
Vol 135-136 ◽  
pp. 739-742
Author(s):  
Jin Hai Zhang
Keyword(s):  
Identity Authentication ◽  
Fingerprint Recognition ◽  
Theoretical Research ◽  
Identification System ◽  
Fingerprint Matching ◽  
Significant Progress ◽  
Fingerprint Image ◽  
Matching Algorithm ◽  
Minutiae Extraction ◽  
Extraction Algorithm

Fingerprint recognition has wide application prospect in all fields which contain identity authentication. Construction of accurate and reliable，safe and Practical automatic fingerprint identification system(AFIS) has become researc hotspot. Although theoretical research and application developmen of AFIS have got a significant Progress，accuracy of the algorithm and proeessing speeds till need to be improved. In this paper, fingerprint image preprocessing algorithms，fingerprint singular Points and minutiae extraction algorithm and fingerprint matching algorithm are analyzed and discussed in detail.

New Segmentation Scheme for Low Quality Fingerprint Images

2015 ◽  
Vol 742 ◽  
pp. 272-276 ◽  
Author(s):  
Bo Guo ◽  
Bo Han ◽  
Lei Niu
Keyword(s):  
Image Segmentation ◽  
Fingerprint Image ◽  
Fingerprint Segmentation ◽  
Better Than

Proposes a new scheme for low quality fingerprint images which is used point oriental image and based on gray distributing rule of the pixels after investigating existing approaches to fingerprint segmentation. Experiment results indicated that this scheme performs better than traditional fingerprint image segmentation alogrithms. And it has higher performance in terms of efficiency and robustness.

Fingerprint Image Enhancement Algorithm Based on FDCT

2011 ◽  
Vol 255-260 ◽  
pp. 2047-2051 ◽  
Author(s):  
Chong Ben Tao ◽  
Guo Dong Liu
Keyword(s):  
Curvelet Transform ◽  
Input Image ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Threshold Function ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Fast Discrete Curvelet Transform ◽  
Fingerprint Image Enhancement ◽  
Enhancement Algorithm

Fingerprint enhancement is an essential preprocessing step and it is crucial for the efficiency of fingerprint recognition algorithm. We present an enhancement algorithm based on fast discrete curvelet transform (FDCT). First, implement positive transform on input image, namely decompose the image into coarse scales and fine scales coefficients. Then make use of a directional filter and a soft threshold function to enhance image and reduce noise respectively. Finally, implement inverse transform, and reconstruct the enhanced image. Experiments are carried out on FVC2004 databases. For bad quality fingerprints, the results indicate that the proposed algorithm has better enhancement and de-noising effect than traditional methods, and need less time.

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

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4473
Author(s):  
Guo Chun Wan ◽  
Meng Meng Li ◽  
He Xu ◽  
Wen Hao Kang ◽  
Jin Wen Rui ◽  
...  
Keyword(s):  
Poor Performance ◽  
The Self ◽  
Fingerprint Identification ◽  
Identification System ◽  
Segmentation Method ◽  
Fingerprint Image ◽  
Fingerprint Segmentation ◽  
Performance Rate ◽  
Frequency Coherence ◽  
Local Gradient

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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