Fingerprint recognition using wavelet domain features

Individual recognition systems with high-speed and high-accuracy have been recently demanded in the automatic logging into a PC, the immigration at the airport, the access control and diligence & indolence management in an office, and so on. Biometric authentication is now being regarded as the most valid method because of the receptivity, individuality and invariability of biometric identifiers. Various types of the individual recognition systems based on biometrics have been studied and partially realized. Fingerprints, faces, hand geometry, irises, vein patterns, gait, signatures, etc., are known as biometric identifiers. In particular, the fingerprint recognition system has been widely used because of its high reliability and reasonable price (Maltoni et al., 2003a; Jain et al., 2010). The fingerprint recognition methods can be classified into the following three types: (i) the minutiae-based, (ii) the frequency-based and (iii) the image-based methods. The minutiae-based method is mainly being used in the practical fingerprint recognition system. For example, a memetic fingerprint matching algorithm has been recently proposed to identify the optimal or near optimal global matching between two minutiae sets (Sheng et al., 2007). A matching technique for fingerprint recognition using the Minutia CylinderCode (MCC), which is based on 3D data structures built from minutiae distances and angles, has also been made a proposal (Cappelli et al., 2010) to exclude the drawbacks in the fingerprint authentication using local minutiae structures. Moreover, a fingerprint verification using spectral minutiae representations has been suggested to overcome translation, rotation and scaling which are the drawbacks of minutiae-based algorithms (Xu et al., 2009a, 2009b). The frequency-based method, such as the frequency analysis method, is also being used in the practical fingerprint recognition system in order to secretly hide the original fingerprint information (Takeuchi et al., 2007). Recently, a fingerprint recognition method based on melfrequency cepstral coefficients and polynomial shape coefficients has been proposed because of the robustness to noise and the insensivity to translation (Hashad et al., 2010). The image-based method has been being studied to improve the accuracy in the fingerprint recognition. For example, an enhanced image-based algorithm for fingerprint verification based on invariant moment features has been recently proposed to improve matching accuracy and processing speed (Yang & Park, 2008a, 2008b). A novel image-based fingerprint matcher based on the minutiae alignment has also been made a proposal to improve the verification performance (Nanni & Lumini, 2009).

<p>Fingerprint image matching is an important procedure in fingerprint recognition. Robust fingerprint image matching under a variety of different image capture conditions is difficult to achieve, because of changes in finger pressure, variation of the angle, etc. Fingerprint matching is very important for the development of fingerprint system recognition that is sensitive to finger pressure. This paper proposes a fingerprint matching algorithm that enables the so-called fingerprint template (extracted specific part (region of interest (ROI)) of a person’s fingerprints to be matched to the different fingerprint of the same person or different people taken on different time, angle and a different finger pressure using normalized cross-correlation (NCC). This algorithm was implemented in MATLAB. The results showed that the maximum NCC value for ROI of the source fingerprints and targets that was greater than 0.62 indicates a strong correlation or similarity.</p><p> </p>

With the fast development of computer and information technology, multimedia data has become the most important form of information media. Auditory information plays an important role in information location, this comes from the fact that it can be difficult to find useful information. Thus audio classification becomes more important in audio analysis as it prepares for content-based audio retrieval. There is quite a bit of research on the topic of audio classification methods, audio feature analysis, and extraction based on audio classification. Many works of literature extract features of audio signals based on time or Fourier transform frequency domain. The emergence of the wavelet theory provides a time-frequency analysis tool for signal analysis. Wavelet transformation is a local transformation of the signal in time and frequency which can effectively extract information from the signal, and perform multi-scale refinement analysis on functions or signals through operations such as stretching and translation instead of the traditional Fourier transformation. In the time-frequency analysis of the signal, the wavelet analysis captures the local time and frequency characters of the signal which can improve the ability of signal analysis. It can also change certain locals of the signal without affecting other aspects of it. In this paper, the frequency domain features are combined with the wavelet domain features. At the same time that the MFCC features are extracted, the discrete wavelet transform is used to extract the features of the wavelet domain. Then the statistical features are extracted for each audio example, and the SVM model is used to realize the different forms of audio classification identification.

A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution

SummaryWith the improvement of informatization and the development of computer technology, identity recognition with fingerprint has been the growing trend, but the stained fingerprint will make recognition difficult. To solve the above problem, this paper briefly introduced the traditional point matching fingerprint recognition algorithm and the damaged fingerprint recognition method based on the convolution neural network (CNN). Then in MATLAB software, the damaged fingerprint recognition method based on CNN was simulated and compared with the traditional point matching recognition method and traditional CNN recognition method. The results showed that the improved CNN recognition method iterated fewer times and achieved smaller training error during the training; the improved CNN method had lower false recognition rate and rejection rate in identifying the unknown fingerprint; the traditional point matching method spent the most time in identifying the unknown fingerprint, followed by the traditional CNN method and the improved CNN recognition method. This study used CNN for fingerprint recognition and improved CNN to improve its recognition accuracy for the damaged fingerprint. The improved CNN can effectively enhance the recognition accuracy of the damaged fingerprint, which provides a useful reference for the improvement of the accuracy and applicability of the fingerprint recognition system.

The embedded multi-resolution coding in JPEG2000 make the ROI(Region Of Interesting) coding very successful. But it is processed in the wavelet domain, resulting in having to decompose some mature JPEG2000 baseline system coder and decoder to implement the ROI function. So a low computational complexity upscaling method in space domain, deduced from the relation of the original image and the image after wavelet transform, is proposed in this paper without modifying the baseline system. By the method, we should right shift the data outside the region of interesting, then use the normal JPEG2000 system to encode and decode the image, and subsequently we can get the image by left shift that data in the space domain after decoding. The method can get high performance quality in ROI in very low bit rate. And it has low computational complexity, needn't deduce the ROI mask in the wavelet domain. As Maxshift method in the wavelet domain, the Maxshift in space domain can be deduced and it decodes the compressed image without ROI mask. That is the ROI mask transmission is not need. The cost of the simpleness is that it will produce distinct tiring edge around the ROI. The method can implement the ROI function through simple extends with the JPEG2000 baseline hardware without the ROI function and it is significative in practice.

There are various types of applications for fingerprint recognition which is used for different purposes. Fingerprint is one of the challenging pattern Recognition problem. The Fingerprint Recognition system is divided into four stages. First is Acquisition stage to capture the fingerprint image, The second is Pre-processing stage to enhancement, binarization, thinning fingerprint image. The Third stage is Feature Extraction Stage to extract the feature from the thinning image by use minutiae extractor methods to extract ridge ending and ridge bifurcation from thinning. The fourth stage is matching(Identification, Verification) to match two minutiae points by using minutiae matcher method in which similarity and distance measure are used. The algorithm is tested accurately and reliably by using fingerprint images from different databases. In this paper the fingerprint databases used are FVC2000 and FVC2002 Databases, we see that, the FVC2002 database perform better results compare with FVC2000 database. The recognition system evaluate with two factor FAR and FRR, In this system the result of FAR is 0.0154 and FRR is 0.0137 with Accuracy equal to 98.55%.

The issue of ownership of digital watermark is addressed in this paper. An audio watermarking scheme is presented where the biometric pattern of iris is used to generate the watermark that has a stamp of ownership. The watermark is embedded in the high-energy regions selectively in wavelet domain that makes the embedding process robust against signal processing attacks. The watermark extraction process is blind and does not require the original host audio signal. Experimental results of listening evaluation and robustness tests have confirmed that the proposed scheme maintains good perceptual property and offers strong resistance to typical signal processing attacks.

Fingerprint recognition is one of the most important challenging tasks of the biometrics identification due to the fact that they are easy to obtain and most importantly they are reliable. A number of pattern recognition methods have been used to perform fingerprint matching. Among such methods fingerprint matching based on minutia features is one of the researched problems. So we proposed effective fingerprint matching algorithm based on feature extraction. Also we have presented a novel fingerprint recognition technique for minutia extraction and a minutia matching. For minutia marking we considered one special false minutiae removal method and for minutiae matching we considered alignment based method. Experimental results show that this method achieves a much better matching performance.

Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases.

Fingerprint matching algorithm is a key step in fingerprint recognition system. Though there are many existing matching algorithms, there has been inability to match fingerprints in linear time. In this paper we present a novel biometric approach to match fingerprints that run in linear time. We match the minutiae in the fingerprint by constructing a Nearest Neighbor Vector (NNV) considering its k-nearest neighbors. The consolidation of these matched minutiae points is done by incorporating them in binary tree that propagates simultaneously in both fingerprints. This helps our algorithm to run in O(n) time in contrast to many existing algorithms when reference core point is available. We analyze the resulting improvement in computational complexity and present experimental evaluation over FVC2002 database.

Fingerprint has been adjudged as the most reliable means of identification and authentication of an individual because of its uniqueness, high immutability and unchanging patterns. Typically, the ridges of fingers consist of different pattern types whose attributes are often extracted for matching during fingerprint classification. The occurrence of these patterns vary among fingerprint, which has significantly affected the accuracy of fingerprint recognition and classification processes. This paper therefore focuses on the investigation of the impact of pattern variation, singular points and feature points on fingerprint matching. The investigation was based on benchmarked FVC2000, FVC2002, FVC2004 and FVC2006 fingerprint databases which comprise four datasets each from different sources and of varied types. The obtained false non match rate (FNMR), false match rate (FMR), total matching time (TMT) and average matching time (AMT) values revealed that use of multiple matching criteria will lead to extension in the fingerprint matching time.

A novel track-to-track association algorithm based on multiple 1-D discrete wavelet transform (DWT) has been proposed. Since the signal and noise exhibit different features in wavelet domain, the ratio of energy in high resolution wavelet space to energy in low resolution scale space is introduced to establish hypothesis test model based on F-distribution. Two test methods have been presented to resolve the multi-dimension problem. Computer simulation results show that the algorithm is effective and has a good performance to distinguish two closely cross tracks or two closely parallel tracks as well.

The analysis of anti-nuclear antibodies in HEp-2 cells by Indirect Immunofluorescence (IIF) is considered a powerful, sensitive, and comprehensive test for auto-antibodies analysis for autoimmune diseases. The aim of this study is to explore the use of wavelet texture analysis for automated categorization of auto-antibodies into one of the six categories of immunofluorescent staining. Gray level co-occurrence matrix (GLCM) features were extracted over sub-bands obtained from multi-level wavelet decomposition. In this study, an attempt is also made to investigate effect of different wavelet bases and their superiority on spatial domain features on classification task at hand. A qualitative as well as quantitative comparison is done between GLCM features in wavelet domain and spatial domain. Discrete Meyer wavelet has been found to be the most discriminating for this classification task.

Region of interest coding in JPEG 2000