Minutiae Algorithm Research Articles

Fault Diagnosis of Rotating Machinery based on the Minutiae Algorithm

Rotary machinery plays an important role in industry. Combined faults can be observed in rotating machinery, making fault classification difficult. In this paper, the Minutiae algorithm is used to classify the faults from the frequency domain of a particular fault. This paper provides a fault classification technique based on image processing for fault analysis of rotating machinery, recognizing function extraction automatically. Minutiae algorithm, a rising method within the discipline of image processing for characteristic extraction, is utilized in this paper to classify specific faults from the converted recurrence plot. The results reveal the effectiveness of the proposed method, providing a rather powerful tool for fault diagnosis of rotating machinery. The proposed model achieved an accuracy of 100% for combined faults, 98.33% for loosened faults, and 95% for unbalanced faults proving its applicability.

Development of an Improved Multi-filtering Matching Model for Fingerprint Recognition

Over the years research done in the area of fingerprint recognition in which the hybrid matching algorithm is one of the most common techniques, though the hybrid algorithm performed well but still faced with the challenge of false minutiae. This study formulated, simulated, and evaluated a multi-filtering fingerprint matching model to develop a multi-filtering matching model for fingerprint recognition. The method employed a multi-filtering model that was formulated using image pre-processing; minutiae feature extraction, post-processing, and cancellation of false minutiae algorithms in the processed images. The model was simulated using Matlab and fingerprint images from the Fingerprint Verification Competition (FVC) 2002 database. The performance of the model was evaluated using the False Acceptance Rate (FAR), False Rejection Rate (FRR), and Error Equal Rate (EER). The results showed that the false minutiae cancellation algorithm considerably reduced the false minutiae points in the thinned images which resulted in the reduction of false acceptance when two different images were tested, and also reduction in false rejection rate when two same images were tested. The match score was below the threshold value of 50 for false acceptance rate and above the threshold value of 50 for the false rejection rate. The error equal rate EER value of 0.076 was recorded. The study concluded that there was a significant reduction in the false minutiae points present in the thinned images and that a high accuracy of fingerprint matching was achieved when the datasets include poor quality fingerprint images.

A discrete wavelet transform-based audio watermarking technique for digital security

This work proposes a model using discrete wavelet transform (DWT) to eliminate the attacks on digital security. Semi-fragile watermarking techniques strive towards establishing the confidentiality of data content. Minutiae algorithm finds its application in various fields to safeguard and protect the data content ensuring integrity of data security, authenticity, copyright proofs and data ownership. This work is based on the usage of the combined technique of employing the minutiae algorithm along with DWT for the secured retrieval of audio data to protect network security. Experimental results are shown for the proposed technique based on minutiae algorithm and DWT by considering the four parameters - signal-to-noise ratio (SNR), computation time, entropy and IQI. DWT ensures high secured digital network in areas like defense, satellite and related fields.

Automated Student Attendance System using Fingerprint Recognition

The project work aims at designing a student attendance system which could effectively manage attendance of students of the department of Computer Science and Engineering at Jatiya Kabi Kazi Nazrul Islam University. In this project work, attendance is marked after student’s biometric identification. For student identification, a fingerprint recognition based identification system is used. Fingerprint features are considered to be the best and fastest method for biometric identification. These features are more secure to use and unique for every person that don’t change in one's lifetime. Fingerprint recognition is a mature field today, but still identifying individual from a set of enrolled fingerprints is a time taking process. It was very necessary to improve the fingerprint identification system for implementation on large databases, e.g. of an institute or a country. In this project, the minutiae algorithm is used to develop the identification system which is faster in implementation than any other available today in the market. The proposed automated attendance system based on fingerprint recognition was tested on a class of student fingerprint databases and achieved significant results for taking an attendance of the students of the Department of Computer Science and Engineering. The proposed system has been implemented using C# programming paradigm platform.

Undecimated discrete wavelet transform for touchless 2D fingerprint identification

Several recent research efforts in biometrics have focused on developing the touchless fingerprint identification system. Most of them are using imaging resulting from cameras and mobile devices. The acquired images are firstly subjected to robust preprocessing steps to localise region of interest in order to extract its features. In the literature, touchless fingerprint features are generally based on algorithms designed for minutiae analysis in touch-based images. Because of perspective distortions and deformations in the samples, minutiae-based techniques can obtain poor results. This paper investigates multi-resolution decomposition features to overcome the limitations of using traditional minutiae algorithms in term of accuracy and matching speed. These decompositions are implemented on Hong Kong Polytechnic University 2D touchless fingerprint database that contains 10,080 images. Experimental results illustrate successful use of undecimated discrete wavelet transform (UDWT) and discrete wavelet packet transform (DWPT) which give better performance than discrete wavelet transform (DWT) and minutiae-based method with less calculation cost.

HYBRID FINGERPRINT MATCHING ALGORITHM FOR HIGH ACCURACY AND RELIABILITY

This paper introduces the hybrid fingerprint matching algorithm using texture algorithm and minutiae algorithm. These algorithm increases the high accuracy and reliability of the fingerprint images. The proposed texture features are arch texture, whorl texture and loop texture. The minutiae features are ridge ending, short ridge, and bifurcation etc. In this paper, we conclude that, the results are combined between the pattern and minutiae matching score. In minutiae algorithm, images are not well performed in the input images and database images. We present the hybrid algorithm by using texture and pattern algorithm.

Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters

The main task of a fingerprint image enhancement is to enhance the image in such a way that it not only remove the noise but also enhance the reliable minutiae points. For this purpose, in this paper we propose a multi-scale decimation-free directional filter bank method for reliable orientation estimation. This reliable orientation is used in coherence enhancement diffusion and in Gabor filter based enhancement, which overcomes the drawbacks of these two methods. Experimental results show that the proposed method not only enhances the images but also facilitates the minutiae algorithm, by enhancing the true minutiae points.

Advanced Authentication Scheme using Multimodal Biometric Scheme

Fingerprint recognition has attracted various researchers and achieved great success. But, fingerprint alone may not be able to meet the increasing demand of high accuracy in today’s biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. Pre-processing is done for fingerprint and palmprint images separately in order to remove any noise. The next step is feature extraction. Minutiae algorithm is used for fingerprint feature extraction and Local Binary pattern for palmprint. Wavelet fusion is applied in order to fuse the extracted features and Support Vector Machine is used for matching. The main highlight of the project is multimodal biometrics which will give a better security and accuracy comparing to unimodel system.