MARIA: Minutiae Analysis using Rotation Invariant Algorithm

Pokhriyal, Avinash; Lehri, Sushma

doi:10.5769/j201001001

Cited by 3 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The skeleton is a black-and-white image where all ridge lines are 1 pixel wide. Two methods of obtaining fingerprint skeleton are described in [7] and [17]. The fingerprint skeleton is very convenient representation for automated pattern recognition.…”

Section: Motivation and Problem Analysismentioning

confidence: 99%

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Marák

Hambalík

2016

Tatra Mountains Mathematical Publications

View full text Add to dashboard Cite

Performance of modern automated fingerprint recognition systems is heavily influenced by accuracy of their feature extraction algorithm. Nowadays, there are more approaches to fingerprint feature extraction with acceptable results. Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks. Many papers have demonstrated uses of neural networks in fingerprint recognition, but there is a little work on using them as Level-2 feature extractors. Our goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints. In this work, we investigated possibilities of incorporating artificial neural networks into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose impact on feature extraction accuracy and overall recognition rate was evaluated. The result of this research is a fully functional software system for fingerprint recognition that consists of fingerprint sensing module using high resolution sensor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fingerprint matching module using the industry standard BOZORTH3 matching algorithm. For purposes of evaluation we used more fingerprint databases with varying image quality, and the performance of our system was evaluated using FMR/FNMR and ROC indicators. From the obtained results, we may draw conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.

show abstract

Section: Motivation and Problem Analysismentioning

confidence: 99%

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Marák

Hambalík

2016

Tatra Mountains Mathematical Publications

View full text Add to dashboard Cite

show abstract

“…This method is called MERIT (Minutiae Extraction using Rotation Invariant Thinning), as it thins a fingerprint image irrespective of the fingerprint's position and then extracts minutiae points from a fingerprint image. [14] Iwasokun G. B., Akinyokun O. C., Alese B. K. & Olabode O. compared each of the features of a template fingerprint image with each of the features in the feature sets in the reference database to determine whether the template and each of the reference images are from the same source. Comparison is done on the basis of preset parameters such as feature type, location, orientation and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Multimodal Biometric Identification and Verification System using Two Fingerprints

Jain¹,

Sharma²,

Pal³

2013

IJCA

View full text Add to dashboard Cite

Fingerprint recognition is one of the research hotspots of biometrics techniques. Fingerprints are the most widely used biometric feature for identification and verification in the field of biometrics. [1]. The traditional fingerprint recognition systems have such disadvantages as high computation complexity, low speed, low recognition rate to uncompleted or defiled fingerprints, and not robust [2]. In this paper, we propose a multimodel fingerprint identification and verification method based on pattern recognition, which emphasizes global features of fingerprint. With lots of artificial fingerprint samples, the results show that the proposed method is effective, fast, robust and shows the Improvement in rate of false acceptance and false rejections. Experimental results are analyzed and a fingerprint recognition system is introduced.

show abstract

“…Finally, some postprocessing is done to remove false minutiae structures on the thinned fingerprint image [2].…”

Section: Introductionmentioning

confidence: 99%

Quality Improvement of Fingerprint Recognition System

Chandana¹,

Yadav²,

Mathuria³

2016

Proceedings of the International Congress on Information and Communication Technology

View full text Add to dashboard Cite

It is a great achievement for us to digitally store and match digital fingerprint to recognize human. But still there are some significant modifications required to improve the quality of fingerprint recognition system's result. Most of the digital fingerprint image contains noise. This unwanted information can be removed using preprocessing by image enhancement which includes various types of filtering techniques and binarization. Then the postprocessing operation which includes the computations of minutiae points and minutiae matching operation is performed. The research work is analyzed by comparing the output of filters. It exposed that unsharp filter gives more output values as compared to other filters that is better for blur fingerprint images.

show abstract

MARIA: Minutiae Analysis using Rotation Invariant Algorithm

Cited by 3 publications

References 9 publications

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Development of a Multimodal Biometric Identification and Verification System using Two Fingerprints

Quality Improvement of Fingerprint Recognition System

Contact Info

Product

Resources

About