Fingerprint Recognition Using Local Features and Hu Moments

Aguilar-Torres, Gualberto; Sánchez-Pérez, Gabriel; Toscano-Medina, Karina

doi:10.22201/icat.16656423.2012.10.5.366

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…Finally, the overall results can be summarized in this section. As shown in the previous tables (2)(3)(4)(5)(6)…”

Section: Overall Performance and Comparisonsmentioning

confidence: 77%

“…The suggested the enhanced the performance of verification system. G. Aguilar-Torres et al, [5] proposed fingerprint recognition by using local feature and Hu-moment for verification purpose. The combination of FFT and Gabor filter was used to enhancement the fingerprint images, and they test their methods on FVC2002 dataset and the local feature used such as minutiae feature by using Crossing number (CN) to detect the type of minutiae feature.…”

Section: Related Workmentioning

confidence: 99%

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Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System

Ali

Qaed

Al‐Ahdal

2022

ajobas

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In the biometrics, the technologies grow day by day and the security also increased related to that technologies. The fingerprint was the most intensively researched in the field of biometrics system due to permanence and uniqueness features which made varies of different peoples. The paper addressing many stages, in addition to the primary stages of any biometrics system the fusion of unimodal system was used in order to improve the performance of the system. The double enhancement techniques were used to make the images very clear by Histogram Equalization and Fast Fourier Transformation (FFT). The feature extraction was conducted using three techniques which called Zernike Moment (ZM), Hu-Moments (Hu) and Gray-Level Co-occurrence Matrix (GLCM) that categorized to statistical and texture features. The matching between these features was performed using the Euclidean distance to find the scores matrix. Additionally, the fusion as the most modern technique was used to improve the performance of the biometrics system which performed in this work by feature level and score level fusions. The feature level fusion by using concatenation and score level fusion by using Weight sum rule strategy led to improve the performance of the system. The system was evaluated by False Accepted Rate (FAR), False Rejected Rate (FRR), Equal Error Rate (EER) and Genuine Accept Rate (GAR). The results show that, the fusion gave the most efficiency results compared with individual system. The work was tested on four datasets such as Fingerprint Verification Competition (FVC2000), (FVC2002), (FVC2004) and our department datasets which called KVK dataset. The best results were achieved by FVC2002 with maximum GAR reached to 98.45% and minimum EER of 1.54% as compared with other datasets and existing works.

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“…Finally, the overall results can be summarized in this section. As shown in the previous tables (2)(3)(4)(5)(6)…”

Section: Overall Performance and Comparisonsmentioning

confidence: 77%

Section: Related Workmentioning

confidence: 99%

Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System

Ali

Qaed

Al‐Ahdal

2022

ajobas

View full text Add to dashboard Cite

show abstract

“…Nowadays, some researchers start studying the algorithm of fingerprint recognition based on local features [18,19,20,21]. Aguilar-Torres et al [22] presented a fingerprint recognition method using a combination of the Fast Fourier Transform (FFT) with Gabor filters for image enhancement, and fingerprint recognition was carried out using a novel recognition stage based on Local Features and Hu invariant moments for verification. Ceguerra et al [23] presented a new approach for combining local and global recognition schemes for automatic fingerprint verification (AFV), by using matched local features as the reference axis for generating global features.…”

Section: Related Workmentioning

confidence: 99%

A Pseudo-Splicing Algorithm for Partial Fingerprint Recognition Based on Sift

Zhu¹,

Li²,

Jiang³

et al. 2018

Computer Science &Amp; Information Technology (CS &Amp; IT)

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At present, many fingerprint recognition techniques are applied to public infrastructures. Their targets are mainly for normal-sized fingerprints. However, with the rise of small-sized fingerprint sensors, the acquired partial fingerprints containing only part of information of the finger, which causes that many researchers change their research directions to partial fingerprint recognition. This paper proposes a SIFT-based pseudo-splicing partial fingerprint recognition algorithm. This algorithm uses the SIFT algorithm to pseudo-splice the input fingerprints during the fingerprint enrollment to increase the robustness of the fingerprint feature database. The comparisons of the accuracy of the recognition among this algorithm, the minutia-based fingerprint recognition algorithm and the fingerprint recognition algorithm based on image similarity, that shows the first performs well. Moreover, this paper proposes an algorithm to evaluate the quality of partial finger print by calculating the invalid blocks of fingerprint image. The result shows that the evaluation algorithm can effectively filter out low-quality fingerprints.

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“…With the advancement in human-computer interaction field, there is a growing demand for secure protection techniques such as face recognition [1]; finger prints recognition [2]; gesture recognition, and gender classification. Generally speaking, the features of an object are of two types; appearance based (global) and geometric based.…”

Section: Introductionmentioning

confidence: 99%

PSO-GA Based Optimized Feature Selection Using Facial and Clothing Information for Gender Classification

Nazir¹,

Majid-Mirza²,

Ali-Khan³

2014

Journal of Applied Research and Technology

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Gender classification is a fundamental face analysis task. In previous studies, the focus of most researchers has been on face images acquired under controlled conditions. Real-world face images contain different illumination effects and variations in facial expressions and poses, all together make gender classification a more challenging task. In this paper, we propose an efficient gender classification technique for real-world face images (Labeled faces in the Wild). In this work, we extracted facial local features using local binary pattern (LBP) and then, we fuse these features with clothing features, which enhance the classification accuracy rate remarkably. In the following step, particle swarm optimization (PSO) and genetic algorithms (GA) are combined to select the most important features' set which more clearly represent the gender and thus, the data size dimension is reduced. Optimized features are then passed to support vector machine (SVM) and thus, classification accuracy rate of 98.3% is obtained. Experiments are performed on real-world face image database.

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Fingerprint Recognition Using Local Features and Hu Moments

Cited by 5 publications

References 13 publications

Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System

Score and Feature Level Fusion Approaches for Evaluation of Multi-Features of Fingerprint Modality for Person Recognition System

A Pseudo-Splicing Algorithm for Partial Fingerprint Recognition Based on Sift

PSO-GA Based Optimized Feature Selection Using Facial and Clothing Information for Gender Classification

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