2D-Gabor Filter Design and Parameter Selection Based on Iris Recognition

2014 ◽  
Vol 11 (6) ◽  
pp. 1995-2002 ◽  
Author(s):  
Guang Huo
Keyword(s):  
Iris Recognition ◽  
Filter Design ◽  
Gabor Filter ◽  
Parameter Selection ◽  
2D Gabor Filter

Study on the Iris Recognition Technology Based on 2D-Gabor Filter

2014 ◽  
Vol 539 ◽  
pp. 151-155
Author(s):  
Wei Bin Mu ◽  
Shu Li Zhang ◽  
Xiao Dong Wang ◽  
Jing Yu Li
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Single Frequency ◽  
Multiple Channels ◽  
Extraction Stage ◽  
Feature Values ◽  
2D Gabor Filter ◽  
Filter Frequency

Iris recognition algorithm mainly includes three parts: positioning segmentation, normalization, and feature extraction. In the feature extraction stage, the filter frequency suitable for extracting iris feature value is calculated in this paper, and then a single-frequency 2D-Gabor filter is designed for coding and matching normalized iris images. Through the experimental verification, feature values can be extracted with a single-frequency 2D-Gabor filter, so as to implement iris recognition. Single frequency is more applicable than multiple channels.

IMAGE THRESHOLDING BASED ON HIERARCHICAL CLUSTERING ANALYSIS AND PERCENTILE METHOD FOR TUNA IMAGE SEGMENTATION

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Alifia Puspaningrum ◽  
Nahya Nur ◽  
Ozzy Secio Riza ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Hierarchical Clustering ◽  
Gabor Filter ◽  
Hierarchical Cluster ◽  
Experimental Result ◽  
Main Process ◽  
Percentile Method ◽  
Cluster Analysis Method ◽  
2D Gabor Filter

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

scholarly journals A Note on Advantages of the Fuzzy Gabor Filter in Object and Text Detection

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 678
Author(s):  
Vladimir Tadic ◽  
Tatjana Loncar-Turukalo ◽  
Akos Odry ◽  
Zeljen Trpovski ◽  
Attila Toth ◽  
...  
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Gabor Filter ◽  
Low Cost ◽  
Fuzzy Optimization ◽  
Detection Performance ◽  
Text Detection ◽  
License Plate ◽  
2D Gabor Filter ◽  
Fine Tune

This note presents a fuzzy optimization of Gabor filter-based object and text detection. The derivation of a 2D Gabor filter and the guidelines for the fuzzification of the filter parameters are described. The fuzzy Gabor filter proved to be a robust text an object detection method in low-quality input images as extensively evaluated in the problem of license plate localization. The extended set of examples confirmed that the fuzzy optimized Gabor filter with adequately fuzzified parameters detected the desired license plate texture components and highly improved the object detection when compared to the classic Gabor filter. The robustness of the proposed approach was further demonstrated on other images of various origin containing text and different textures, captured using low-cost or modest quality acquisition procedures. The possibility to fine tune the fuzzification procedure to better suit certain applications offers the potential to further boost detection performance.

Iris Recognition Method Based on Feature Discrimination and Category Correlation

2014 ◽  
Vol 602-605 ◽  
pp. 1610-1613
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Song Li
Keyword(s):  
Iris Recognition ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Texture Characteristic ◽  
Multi Scale ◽  
Laplacian Score ◽  
Circular Gabor Filter ◽  
Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

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