The Enhancement of Fingerprint Images using Gabor Filter

Erwin, Erwin; Karo, Nina Nuria Br.; Sari, Asti Yulia; Aziza, Nur; Putra, Hadrians Kesuma

doi:10.1088/1742-6596/1196/1/012045

Cited by 13 publications

(3 citation statements)

References 5 publications

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“…• Steps to perform texture analysis -Apply gabor filter (Choose Gabor filter parameters: filter size, orientation, wavelength, standard deviation, phase offset, and aspect ratio). Figure 4 shows the filtered image using Gabor filer (17) . • Flattening the features Concatenate the extracted GLCM features (contrast, energy, correlation, homogeneity) with the Fourier descriptors.…”

Section: Glcmmentioning

confidence: 99%

Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Poornima,

Srinath

2024

IJST

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Objectives: The objective of this paper is to introduce and demonstrate an innovative approach for the recognition of Indian sign language gestures, with a focus on bridging communication gap between the deaf and hearing communities. The goal is to contribute to the development of effective tools and technologies that facilitate seamless communication between individuals using sign language and the people with no knowledge about sign language. Methods: The methodology consists of three key steps. First, data pre-processing involves resizing and contours extraction. Next, feature extraction employs Fourier descriptors for frequency domain analysis and gray-level-co-occurrence matrix for spatial domain analysis. Finally, various machine learning models including SVM, Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes are trained on a standard dataset. Findings: In our controlled experimental setup, we applied a diverse set of machine learning classifiers to evaluate the proposed approach for gesture recognition. Among the classifiers tested, K-Nearest Neighbors demonstrated the highest accuracy, achieving 99.82%. To validate the robustness of our approach, we employed k-fold cross-validation with 5 folds. Novelty: This study presents an innovative method for sign language recognition by employing a dual-domain fusion strategy that prominently emphasizes the frequency domain. Through the integration of Fourier descriptors, the research conducts a detailed frequency domain analysis to characterize the contour shapes of sign language gestures. The synergy with gray-level co-occurrence matrix texture features in the spatial domain analysis, contributes to the creation of a comprehensive feature vector. The proposed approach ensures a thorough exploration of gesture features, there by advancing the precision and efficacy of sign language recognition. Keywords: Indian Sign Language (ISL), Sign Language Recognition (SLR), Frequency domain, Spatial domain, Fourier descriptors, Gray level cooccurrence matrix (GLCM), K Fold

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Section: Glcmmentioning

confidence: 99%

Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Poornima,

Srinath

2024

IJST

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“…Hence, the input fingerprint images need to be preprocessed. e preprocess for fingerprint images can be done using some processes such as image enhancement, normalization, filtering, noise reduction, binarization, and thinning [36]. In this research, binarization and thinning are applied since they are widely used before fingerprint classification and matching.…”

Section: Preprocessingmentioning

confidence: 99%

Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection

Alsharman

Saaidah

Almomani

et al. 2022

Security and Communication Networks

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Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.

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“…Gabor filters are formed from two components, sinusoidal and Gaussian, The Gabor function was discovered by Gabor in 1946, where the function is defined in 1-D with stating time and then developed unto 2-D in the spatial domain formulated [25].…”

Section: Gabor Filtersmentioning

confidence: 99%

Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image

Ketmaneechairat¹,

Maliyaem²,

Intarat³

2021

Adv. sci. technol. eng. syst. j.

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Identification of Kamphaeng Saen beef cattle is important of the registration and traceability purposes. For a traditional identification methods, Hot Branding, Freeze Branding, Paint Branding, and RFID Systems can be replaced by genius human. This paper proposed a Kamphaeng Saen beef cattle identification approach using muzzle print images as an Animal Biometric approach. There are two algorithms used in the system: Scale Invariant Feature Transform (SIFT) for detecting the interesting points and Random Sample Consensus (RANSAC) algorithm used to remove the outlier points and then to achieve more robustness for image matching. The image matching method for Kamphaeng Saen beef cattle identification consists of two phases, enrollment phase and identification phase. Beef cattle identification is determined according to the similarity score. The maximum estimation between input image and one template is affected from two perspectives. The first perspective applied SIFT algorithm in the size of the moving image with the rotating image and applied Gabor filters to enhance the image quality before getting the interesting points. For a robust identification scheme, the second perspective applied the RANSAC algorithm with SIFT output to remove the outlier points to achieve more robustness. Finally, feature matching is accomplished by the Brute-Force Matchers to optimize the image matching results. The system was evaluated based on dataset collected from Kamphaeng Saen (KPS; 47 cattle, 391 images), Nakhon Pathom and Tubkwang (TKW; 39 cattle, 374 images), Saraburi, Thailand. The muzzle print images database was collected between 2017 and 2019, in the total of 765 muzzle print images from 86 different cattles. The experimental result is given 92.25% in terms of accuracy which better than a traditional identification approach. Therefore, muzzle print images can be used to identify a Kamphaeng Saen beef cattle for breeding and marking systems.

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The Enhancement of Fingerprint Images using Gabor Filter

Cited by 13 publications

References 5 publications

Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection

Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image

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