A Model-Based Approach for Compression of Fingerprint Images

We propose a new fingerprint image compression scheme based on the hybrid model of an image. Our scheme uses the essential steps of a typical automated fingerprint identification system (AFIS) such as enhancement, binarization and thinning to encode fingerprint images. The decoding process is based on reconstructing a hybrid surface by using the gray values on ridges and valleys. In this compression scheme, the ridge skeleton is coded efficiently by using differential chain codes. The valley skeleton is derived from the ridge skeleton and the gray values along the ridge and valley skeletons are encoded using the discrete cosine transform. The error between the original and the replica is also encoded to increase the quality. One advantage of our approach is that original features such as end points and bifurcation points can be extracted directly from compressed image even for a very high compression ratio. Another advantage is that the proposed scheme can be integrated to a typical AFIS easily. The algorithm has been applied to various fingerprint images, and high compression ratios like 63:1 have been obtained. A comparison to wavelet/scalar quantization (WSQ) has been also made

An experimental study on the deformation behaviour and fracture mode of recycled aluminium alloy AA6061-reinforced alumina oxide undergoing high-velocity impact

The anisotropic behaviour and the damage evolution of recycled aluminium alloy-reinforced alumina oxide are investigated in this paper using Taylor impact test. The test is performed at various impact velocity ranging from 190 to 360 m/s by firing a cylindrical projectile towards anvil target. The deformation behaviour and the fracture modes are analysed using the digitized footprint of the deformed specimens. The damage initiation and the progression are observed around the impact surface and the surface 0.5 cm from the impact area using the scanning electron microscope. The deformed specimens showed several ductile fracture modes of mushrooming, tensile splitting and petalling. The critical impact velocity is defined below 280 m/s. The specimens showed a strong strain-rate dependency due to the damage evolution that is driven by severe localized plastic-strain deformation. The scanning electron microscope analysis showed the damage mechanism progress via voids initiation, growth and coalescence in the material. The micrograph within the footprint surface shows the presence of alumina oxide particles within the specimen. The microstructure analysis shows a significant refinement of the specimen particle at the surface located 0.5 cm above the impact area. ImageJ software is adopted in this work to measure the average size of voids within this surface. Non-symmetrical (ellipse-shaped) footprint around the footprints showed plastic anisotropic behaviour. The results in this paper provide a better understanding of the deformation behaviour of recycled materials subjected to dynamic loading. This information on mechanical response is crucial before any potential application can be established to substitute the primary sources

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

Using SVD and DWT Based Steganography to Enhance the Security of Watermarked Fingerprint Images

Watermarking is the process of embedding information into a carrier file for the protection of ownership/copyright of digital media, whilst steganography is the art of hiding information. This paper presents, a hybrid steganographic watermarking algorithm based on Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transforms in order to enhance the security of digital fingerprint images. A facial watermark is embedded into fingerprint image using a method of singular value replacement. First, the DWT is used to decompose the fingerprint image from the spatial domain to the frequency domain and then the facial watermark is embedded in singular values (SV’s) obtained by application of SVD. In addition, the original fingerprint image is not required to extract the watermark. Experimental results provided demonstrate the methods robustness to image degradation and common signal processing attacks, such as histogram and filtering, noise addition, JPEG and JPEG2000 compression with various levels of quality

Study of Fingerprint Enhancement and Matching

Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

FLAG : the fault-line analytic graph and fingerprint classification

Fingerprints can be classified into millions of groups by quantitative measurements of their new representations - Fault-Line Analytic Graphs (FLAG), which describe the relationship between ridge flows and singular points. This new model is highly mathematical, therefore, human interpretation can be reduced to a minimum and the time of identification can be significantly reduced. There are some well known features on fingerprints such as singular points, cores and deltas, which are global features which characterize the fingerprint pattern class, and minutiae which are the local features which characterize an individual fingerprint image. Singular points are more important than minutiae when classifying fingerprints because the geometric relationship among the singular points decide the type of fingerprints. When the number of fingerprint records becomes large, the current methods need to compare a large number of fingerprint candidates to identify a given fingerprint. This is the result of having a few synthetic types to classify a database with millions of fingerprints. It has been difficult to enlarge the minter of classification groups because there was no computational method to systematically describe the geometric relationship among singular points and ridge flows. In order to define a more efficient classification method, this dissertation also provides a systematic approach to detect singular points with almost pinpoint precision of 2x2 pixels using efficient algorithms

Fingerprint Image Compression Using Wavelet Transform

The fingerprint is considered to be the most reliable kind of personal identification because it cannot be forgotten, misplaced, or stolen. Fingerprint authorization is potentially the most affordable and convenient method of verifying a person's identity. Storage of fingerprint image databases needs allocation of huge secondary storage devices. To reduce the increasing demand on storage space, efficient data compression techniques are needed. In addition to that, the exchange of fingerprint images between the governmental agencies could be done fast. The compression algorithm must also preserve original information in the original image. Digital image compression based on the ideas of subband decomposition or discrete wavelet transform (DWT) has received much attention in recent years. In fact, wavelet refers to a set of basic function, which is recursively defined form, a set of scaling coefficients and scaling function. Discrete Wavelet Transform CDWT) represents images as a sum of wavelet function on different resolution level. Essential for wavelet transform can be composed of any function that satisfies requirements of multi-resolution analysis. It means that there exists a large selection of wavelet families depending on choice of wavelet function. The objective of this study is to evaluate a variety of wavelet filters using Wavelet toolbox for selecting the best wavelet filters to be used in compress and decompress of selected fingerprint images. Therefore a two-dimensional wavelet decomposition, quantization and reconstruction using several families of filter banks were applied to a set of fingerprint images. The results show that no specific wavelet filter performs uniformly except for Biorthogonal and Symlets, and that is using the matching technique. The result shows that at a threshold value equal of 160 and decomposition level 3 with a wavelet filter sym4, there is no difference between the original and reconstructed image. This study concludes that using wavelet filters sym4 and bior3.7 can achieve compression ratio 27: 1 with PSNR 20.36 dB and 17: 1 with PSNR 21.88 dB respectively. These values indicate that using these filters, the quality of the reconstructed fingerprint still exist