Multi-resolution Gabor wavelet feature extraction for needle detection in 3D ultrasound

2015 ◽  
Author(s):  
Arash Pourtaherian ◽  
Svitlana Zinger ◽  
Nenad Mihajlovic ◽  
Peter H. N. de With ◽  
Jinfeng Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
3D Ultrasound ◽  
Gabor Wavelet ◽  
Needle Detection

scholarly journals Circular Gabor wavelet algorithm for fingerprint liveness detection

2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Olufade F.W. Onifade ◽  
Paul Akinde ◽  
Folasade Olubusola Isinkaye
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Distinct Difference ◽  
Identification Algorithm ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Total Internal Reflectance ◽  
Internal Reflectance ◽  
Wavelet Algorithm

Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.   

scholarly journals Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Sulayman Ahmed ◽  
Mondher Frikha ◽  
Taha Darwassh Hanawy Hussein ◽  
Javad Rahebi
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Recognition System ◽  
Gabor Wavelet ◽  
Swarm Optimization ◽  
Gabor Wavelet Transform ◽  
Face Recognition System

In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

Approach of Using Texture and Shape for Image Retrieval

2013 ◽  
pp. 30-45
Author(s):  
Gang Zhang ◽  
Zongmin Ma ◽  
Li Yan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Feature Integration ◽  
Shape Features ◽  
Important Research ◽  
Information Redundancy ◽  
Single Feature ◽  
Descriptor Approach

Feature integration is one of important research contents in content-based image retrieval. Single feature extraction and description is foundation of the feature integration. Features from a single feature extraction approach are a single feature or composite features, whether integration features are more discriminative than them or not. An approach of integrating shape and texture features was presented and used to study these problems. Gabor wavelet transform with minimum information redundancy was used to extract texture features, which would be used for feature analyses. Fourier descriptor approach with brightness was used to extract shape features. Then both features were integrated in parallel by weights. Comparisons were carried out among the integration features, the texture features, and the shape features, so that discrimination of the integration features can be testified.

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