Fast Robust Fingerprint Feature Extraction and Classification

Nyongesa, Henry O.; Al-Khayatt, Samir; Mohamed, Suziyani; Mahmoud, Mohamed

doi:10.1023/b:jint.0000034344.58449.fd

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…In Nyongesa method [58], the authors use a feature vector composed of angle differences between the detected SPs, which are complemented with the global orientation in the fingerprint image.…”

Section: Nyongesamentioning

confidence: 99%

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A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal

Galar

Derrac²,

Peralta

et al. 2015

Knowledge-Based Systems

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In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.

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Section: Nyongesamentioning

confidence: 99%

“…Nyongesa et al [58] proposed the usage of three types of neural networks: multi-layer perceptron (MLP), radial basis function (RBF) and fuzzy neural network (FNN). From the results of the paper, MLP was the one reaching the best accuracy, and this is why we have selected it as the specific classifier for Nyongesa's feature vector.…”

Section: Nyongesamentioning

confidence: 99%

A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal

Galar

Derrac²,

Peralta

et al. 2015

Knowledge-Based Systems

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“…This index takes value 0, 1 2 or − 1 2 , indicating the absence of a SP, the presence of a core or the presence of a delta, respectively. Although other authors have used other configurations of the neighbourhood [17,64], specially regarding its size, we maintain the widely accepted 3 × 3 size.…”

Section: Methodsmentioning

confidence: 99%

A framework for radial data comparison and its application to fingerprint analysis

Marco-Detchart

Cerrón

Miguel

et al. 2016

Applied Soft Computing

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This work tackles the comparison of radial data, and proposes comparison measures that are further applied to fingerprint analysis. First, we study the similarity of scalar and non-scalar radial data, elaborated on previous works in fuzzy set theory. This study leads to the concepts of Restricted Radial Equivalence Function and Radial Similarity Measure, which model the perceived similarity between scalar and vectorial pieces of radial data, respectively. Second, the utility of these functions is tested in the context of fingerprint analysis, and more specifically, in the singular point detection. With this aim, a novel template-based singular point detection method is proposed, which takes advantage of these functions. Finally, their suitability is tested in different fingerprint databases. Different similarity measures are considered to show the flexibility offered by these measures and the behavior of the new method is compared with well-known singular point detection methods.

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“…Many researchers have tried to extract singular points in the flow of the ridges [16]. The authors of References [17][18][19] proposed a heuristic algorithm with singularities to classify fingerprints; the disadvantage of these studies is not focusing on improving image quality. Because they use features of singularity points position.…”

Section: Related Workmentioning

confidence: 99%

Fingerprints Classification through Image Analysis and Machine Learning Method

Nguyen

2019

Algorithms

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The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) 2 / 3 databases.

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Fast Robust Fingerprint Feature Extraction and Classification

Cited by 21 publications

References 9 publications

A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal

A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal

A framework for radial data comparison and its application to fingerprint analysis

Fingerprints Classification through Image Analysis and Machine Learning Method

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