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.

Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values

2017 ◽  
Vol 5 (2) ◽  
pp. 427
Author(s):  
Pakutharivu P ◽  
Srinath M. V
Keyword(s):  
Image Enhancement ◽  
Gabor Filter ◽  
Orientation Angle ◽  
Input Image ◽  
False Identification ◽  
Gabor Filtering ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Frequency Components ◽  
Fingerprint Image Enhancement

<p>Fingerprint image enhancement is the key process in IAFIS systems.  In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced.  A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image.  This paper presents an experimental summary of enhancing fingerprint images using Gabor filters.  Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, ,   and  radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features. </p>

A GABOR FILTER BASED FINGERPRINT ENHANCEMENT SCHEME USING AVERAGE FREQUENCY

2006 ◽  
Vol 20 (03) ◽  
pp. 417-429 ◽  
Author(s):  
EN ZHU ◽  
JIANPING YIN ◽  
GUOMIN ZHANG ◽  
CHUNFENG HU
Keyword(s):  
Gabor Filter ◽  
Average Frequency ◽  
Input Image ◽  
Fingerprint Recognition ◽  
Fingerprint Enhancement ◽  
Filter Tuning ◽  
Recognition Systems ◽  
Fingerprint Minutiae ◽  
Time Critical

Fingerprint minutiae are prevalently used in fingerprint recognition systems. The extraction of fingerprint minutiae is heavily affected by the quality of fingerprint images. This leads to the incorporation of a fingerprint enhancement module in fingerprint recognition systems to make the system robust with respect to the quality of input fingerprint images. Most of existing enhancement methods suffer from two main kinds of defects: (1) time consuming and thus unusable in time critical applications; and (2) blocky and directional effects in the enhanced image. This paper proposes an improved fingerprint enhancement scheme based on the Gabor filter tuning its frequency to the average frequency of the input image and changing its shape from square to circle and dynamically adjusting the filter's size based on the average frequency. This scheme can enhance the fingerprint image rapidly and overcome the blocky and directional effects and does improve the performance of minutiae detection.

scholarly journals Enhancing Fingerprint Image Recognition Algorithm Using Fractional Derivative Filters

2017 ◽  
Vol 7 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Hossein Baloochian ◽  
Hamid Reza Ghaffary ◽  
Saeed Balochian
Keyword(s):  
Fractional Derivative ◽  
Fractional Derivatives ◽  
Gabor Filter ◽  
Input Image ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Direction Estimation ◽  
First Time

Abstract One of the most important steps in recognizing fingerprint is accurate feature extraction of the input image. To enhance the accuracy of fingerprint recognition, an algorithm using fractional derivatives is proposed in this paper. The proposed algorithm uses the definitions of fractional derivatives Riemann-Liouville (R-L) and Grunwald-Letnikov (G-L) in two sections of direction estimation and image enhancement for the first time. Based on it, new mask of fractional derivative Gabor filter is calculated. The proposed fractional derivative-based method enhances the image quality. This method enhances the structure of ridges and grooves of fingerprint, using fractional derivatives. The efficiency of the proposed method is studied in images of FVC2004 (DB1, DB2, DB3 and DB4) database and the results are evaluated using the criteria including entropy, average gradient, and edge intensity. Also, performance of the proposed method is compared with other technical methods such as Gabor filter. Based on the obtained results from the tests, the method is able to enhance the quality of fingerprint images significantly.

scholarly journals An effective and robust fingerprint enhancement by adaptive filtering in frequency domain

2009 ◽  
Vol 22 (1) ◽  
pp. 91-104 ◽  
Author(s):  
Andjelija Raicevic ◽  
Brankica Popovic
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Adaptive Filtering ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Directional Filters

Extensive research of automatic fingerprint identification system (AFIS), although started in the early 1960s, has not yet give the answer to reliable fingerprint recognition problem. A critical step for AFIS accuracy is reliable extraction of features (mostly minutiae) from the input fingerprint image. However, the effectiveness of a feature extraction relies heavily on the quality of the input fingerprint images. This leads to the incorporation of a fingerprint enhancement module in fingerprint recognition system to make the system robust with respect to the quality of input fingerprint images. In this paper we propose an adaptive filtering in frequency domain in order to enhance fingerprint image. Two different directional filters are proposed and results are compared. .

scholarly journals Face–Iris Multimodal Biometric Identification System

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 85 ◽  
Author(s):  
Basma Ammour ◽  
Larbi Boubchir ◽  
Toufik Bouden ◽  
Messaoud Ramdani
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Gabor Filter ◽  
Singular Spectrum Analysis ◽  
Recognition Rate ◽  
Evaluation Process ◽  
Relevant Information ◽  
Recognition System ◽  
Identification System ◽  
New Feature

Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

scholarly journals Feature Based Comparison of Text Based Image Retrieval and Context Based Image Retrieval Images

2018 ◽  
Vol 7 (2) ◽  
pp. 6-11
Author(s):  
Gagandeep Kaur ◽  
Rajeev Kumar Dang
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Gabor Filter ◽  
Vertical Axis ◽  
Noise Removal ◽  
Input Image ◽  
Low Level ◽  
Image Histogram ◽  
Color Correlogram ◽  
Segmentation Image

Image processing is a field to process the images according to horizontal and vertical axis to form some useful results. It deals with edge detection, image compression, noise removal, image segmentation, image identification, image retrieval and image variation etc. Customarily, there are two techniques i.e. text based image retrieval and content based image retrieval that are used for retrieving the image according to features and providing color to all pixel pairs. The system retrieval that is based on TBIR assists to recover an image from the database using annotations. CBIR extorts images to form a hefty degree database using the visual contents of an original image that is called low level features or features of an image. These visual features are extracted using feature extraction and then match with the input image. Histogram, color moment, color correlogram, Gabor filter and wavelet transform are various CBIR techniques that can be used autonomously or pooled to acquire enhanced consequences. This paper states about a novel technique for fetching the images from the image database using two low level features namely color based feature and texture based features. Two techniques- one is color correlogram (for color indexing) and another is wavelet transform (for texture processing) has also been introduced.

scholarly journals A Novel Approach to Address Sensor Interoperability Using Gabor Filter

2017 ◽  
Vol 10 (2) ◽  
pp. 446-453
Author(s):  
Neha Bhatia ◽  
Himani Himani ◽  
Chander Kant
Keyword(s):  
Reliable Method ◽  
Gabor Filter ◽  
Gabor Filters ◽  
Poor Quality ◽  
Significant Loss ◽  
Biometric Authentication ◽  
Biometric System ◽  
Fingerprint Image ◽  
Novel Approach ◽  
Fingerprint Image Enhancement

Biometric authentication using fingerprint is one of the unique and reliable method of verification processes. Biometric System suffers a significant loss of performance when the sensor is changed during enrollment and authentication process. In this paper fingerprint sensor interoperability problem is addressed using Gabor filter and classifying images into good and poor quality. Gabor filters play an important role in many application areas for the enhancement of various types of fingerprint images. Gabor filters can remove noise, preserve the real ridges and valley structures, and it is used for fingerprint image enhancement. Experimental results on the FVC2004 databases show improvements of this approach.

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.

Enhancement of Finger-Vein Image by Vein Line Tracking and Adaptive Gabor Filtering for Finger-Vein Recognition

2011 ◽  
Vol 145 ◽  
pp. 219-223 ◽  
Author(s):  
So Ra Cho ◽  
Young Ho Park ◽  
Gi Pyo Nam ◽  
Kwang Youg Shin ◽  
Hyeon Chang Lee ◽  
...  
Keyword(s):  
Speaker Recognition ◽  
Near Infrared ◽  
Gabor Filter ◽  
Input Image ◽  
Skin Area ◽  
Original Image ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Biometrics is the technology to identify a user by using the physiological or behavioral characteristics. Among the biometrics such as fingerprint, face, iris, and speaker recognition, finger-vein recognition has been widely used in various applications such as door access control, financial security, and user authentication of personal computer, due to its advantages such as small sized and low cost device, and difficulty of making fake vein image. Generally, a finger-vein system uses near-infrared (NIR) light illuminator and camera to acquire finger-vein images. However, it is difficult to obtain distinctive and clear finger-vein image due to skin scattering of illumination since the finger-vein exists inside of a finger. To solve these problems, we propose a new method of enhancing the quality of finger-vein image. This research is novel in the following three ways compared to previous works. First, the finger-vein lines of an input image are discriminated from the skin area by using local binarization, morphological operation, thinning and line tracing. Second, the direction of vein line is estimated based on the discriminated finger-vein line. And the thickness of finger-vein in an image is also estimated by considering both the discriminated finger-vein line and the corresponding position of finger-vein region in an original image. Third, the distinctiveness of finger-vein region in the original image is enhanced by applying an adaptive Gabor filter optimized to the measured direction and thickness of finger-vein area. Experimental results showed that the distinctiveness and consequent quality of finger-vein image are enhanced compared to that without the proposed method.

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