scholarly journals A Critical Study on Fingerprint Image Sensing and Acquisition Technology

2017 ◽  
pp. 86-92
Author(s):  
Krishna Prasad K ◽  
P. S. Aithal
Keyword(s):  
New Technologies ◽  
Digital Camera ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Critical Study ◽  
Identification System ◽  
Fingerprint Matching ◽  
Virtual Camera ◽  
Image Sensing

Automatic Fingerprint Recognition System (AFIS) mainly depends on the quality of the fingerprint captured during the enrollment process, even though a lot of techniques developed in literature for fingerprint matching, all most all system is influenced or affected by the quality of acquisition method. Automated fingerprint identification system requires fingerprint images in a special format. Normally it can't receive and process the photographic image or photo taken from virtual camera or cell camera. There are many special acquisition or sensing strategies to extract the ridge-and-valley structure of finger skin or fingerprint. Traditionally, in law or regulation enforcement packages, fingerprints were especially received offline. Fingerprint acquisition can be specially classified into groups as an offline and live scan. An offline acquisition technique gets input through inked affect of the fingertip on paper and digitized with the aid of the paper with an optical scanner or video digital camera. The live acquisition is received through the sensor that is having the ability to directly digitize the sensing tip of the finger. As the fingerprint sensing, image processing, signal processing, and communication technology advance, an increasing number of new technologies in this acquisition technology are arriving at the main facet. In this paper, we discuss different types of fingerprint acquisition technologies, which involve optical, ultrasonic, capacitance, passive capacitance, and active capacitance. This paper helps to identify new fingerprint acquisition technology.

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 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 Spectral Minutiae Fingerprint Recognition System

2014 ◽  
pp. 103-108
Author(s):  
S. Shanawaz Basha ◽  
N. Musrat Sultana
Keyword(s):  
Large Scale ◽  
Reduction Rate ◽  
Principal Component ◽  
Rejection Rate ◽  
Recognition System ◽  
Feature Reduction ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Behavioral Characteristics ◽  
Template Size

Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics, such as faces, finger prints, iris, and gait. In this paper, we focus on the application of finger print recognition system. The spectral minutiae fingerprint recognition is a method to represent a minutiae set as a fixedlength feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%.With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system, this fast operation renders our system suitable for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems like, police patrolling, airports etc,. The spectral minutiae representation system tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

Application of Fingerprint-Matching Algorithm in Smart Gun Using Touch-Less Fingerprint Recognition System

2020 ◽  
pp. 122-128
Author(s):  
Saifullah Khalid
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fingerprint Matching ◽  
Fingerprint Image ◽  
Matching Algorithm ◽  
Latent Fingerprint ◽  
Recognition Systems

Fingerprint recognition systems are widely used in the field of biometrics. Many existing fingerprint sensors acquire fingerprint images as the user's fingerprint is contacted on a solid flat sensor. Because of this contact, input images from the same finger can be quite different and there are latent fingerprint issues that can lead to forgery and hygienic problems. For these reasons, a touchless fingerprint recognition system has been investigated, in which a fingerprint image can be captured without contact. While this system can solve the problems which arise through contact of the user's finger, other challenges emerge.

scholarly journals Literature Review on Fingerprint Level 1 and Level 2 Features Enhancement to Improve Quality of Image

2017 ◽  
pp. 8-19 ◽  
Author(s):  
Krishna Prasad K. ◽  
P. S. Aithal
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Human Beings ◽  
Global Features ◽  
Basic Premise ◽  
Level 3 ◽  
Level 1 ◽  
The One ◽  
Level 2

Biometrics is the one most popular property in human distinguishing proof based on physical or behavioral features. The different physiological characteristics are Fingerprint, DNA, Face, hand, retina, ear features, and odor, where as behavioral characteristics or features are typing rhythm, gait, gesture, and voice with the basic premise that all are unique and all human beings are identified by these intrinsic traits. In the physiological traits, Fingerprint is most commonly utilized the biometric feature in diverse fields for identification and verification purpose. Fingerprint features can be separated into three noteworthy classifications in view of the granularity at which they are removed as level 1, level 2, and level 3 features. Level 1 feature contains macro details, which are easily extractable and include orientation filed, ridge frequency filed and pattern configuration. Only these global features or Level 1 features are not sufficient to uniquely identify or recognize, but if these features are used along with level 2 or level 3 features, that can make the fingerprint recognition system more robust and secure. Level1 features are used for image enhancement and orientation purpose. In this paper, we made a survey of existing literature on Level 1 features and try to analyze other researcher's contribution to this field.

Fingerprint Recognition in Wavelet Domain

2012 ◽  
Author(s):  
Wan Azizun Wan Adnan ◽  
Tze Siang Lim ◽  
Salasiah Hitam
Keyword(s):  
Image Processing ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Low Cost ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Fingerprint Identification ◽  
Small Computer ◽  
Identification System ◽  
Identification Algorithm

Teknik cetak ibujari merupakan satu daripada teknologi biometrik yang paling boleh diharapkan. Beberapa pendekatan terhadap pemadanan ibujari secara automatik telah dicadangkan dalam saranan. Dalam pengecaman ibujari, pra–prosesan seperti pelicin, binarization dan thinning diperlukan. Kemudian, ciri–ciri cetak ibujari yang terperinci diambil berdasarkan algoritma pengecaman cetak ibujari (seperti dengan menggunakan Fast Fourier Transform (FFT)) mungkin memerlukan teknik–teknik pengkomputeran yang banyak sehingga menjadikannya tidak praktikal. Algoritma berdasarkan wavelet mungkin merupakan kunci untuk membina sistem pengecaman cetak ibujari kos rendah yang boleh dioperasi dalam sistem komputer bermodul kecil. Di sini, satu sistem pengecaman cetak ibujari yang boleh menjalankan pemadanan cetak ibujari berdasarkan kepada ciri–ciri yang diperolehi daripada domain jelmaan wavelet diperkenalkan. Kajian ini adalah berdasarkan kepada perisian MATLAB dan aplikasinya dalam toolbox seperti Wavelet and Image Processing Toolbox. Kata kunci: Biometrik, wavelet, cetaksekuriti, pengecaman cetak ibujari Fingerprint technique is one of the most reliable biometric technologies. In the fingerprint recognition, pre-processing such as smoothing, binarization, and thinning are needed. Then, fingerprint minutia feature is extracted. Some fingerprint identification algorithm (such as using Fast Fourier Transform, (FFT)) may require so much computation as to be impractical. Wavelet based algorithm may be the key to making a low cost fingerprint identification system that would operate on a small computer. We present a fingerprint recognition system that can match the fingerprint images based on features extracted in the wavelet transform domain. This study is implemented based on MATLAB Software and their toolbox applications, such as Wavelet and Image Processing Toolbox. Key words: Biometrics, wavelet, security, fingerprint recognition

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.

scholarly journals A Dual Security Scheme Based on DWT for Personnel Authentication

2019 ◽  
Vol 9 (1) ◽  
pp. 598-602
Keyword(s):  
Rejection Rate ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Discrete Wavelet ◽  
Number Systems ◽  
Biometric Systems ◽  
The Face ◽  
Authentication System ◽  
Facial Recognition System

A biometric identification system that audits the presence of a person using real or behavioral features is safer than passwords and number systems. Present applications are mostly recognize an individual using the single modal biometric system. However, a single characteristic sometimes fails to authenticate accurately. Multimodal biometric technologies solve the problems that exist in the single biometric systems. It is very hard to identify images with low lighting environments using facial recognition system. By utilizing fingerprint recognition, this issue can be better addressed. This paper presents a dual personnel authentication system that incorporates face and fingerprint to improve security. For face identification, the Discrete Wavelet Transform (DWT) algorithm is used to acquire features from the face and fingerprint pictures. The technique used to integrate fingerprint and face is decision level fusion. By adding fingerprint recognition to the scheme, the proposed algorithm decreases the false rejection rate (FRR) in the face and fingerprint recognition and hence increases the accuracy of the authentication.

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