A security system based on human iris identification using wavelet transform

1998 ◽  
Vol 11 (1) ◽  
pp. 77-85 ◽  
Author(s):  
W.W. Boles
Keyword(s):  
Wavelet Transform ◽  
Security System ◽  
Human Iris ◽  
Iris Identification

scholarly journals Performance Analysis of Iris Recognition using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 1-10
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Iris Recognition ◽  
Image Features ◽  
Discrete Wavelet ◽  
Multi Stage ◽  
Human Iris ◽  
The Individual ◽  
Matching Score ◽  
Bicubic Interpolation

Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

Iris Identification System

2016 ◽  
pp. 232-249
Author(s):  
N. Poonguzhali ◽  
M. Ezhilarasan
Keyword(s):  
Performance Metrics ◽  
Texture Features ◽  
Recognition System ◽  
Medical Diagnostics ◽  
Multilayered Structure ◽  
Identification System ◽  
Geometric Features ◽  
Emerging Trends ◽  
Human Iris ◽  
Iris Identification

Recent research on iris is not only on recognition; emerging trends are also in medical diagnostics, personality identification. The iris based recognition system rely on patterns/textures present in the iris, the color of the iris, visible features present in the iris, geometric features of the iris and the SIFT features. An overview of biometric generation is presented. Human iris can be viewed as a multilayered structure in its anterior view. The iris consists of three zones, the pupillary zone, collarette and the ciliary zone. The texture features present in the pupillary zone and collarette are used for identification. As these features are closer to the pupil they are not affected by the occlusion caused by eyelid or eyelashes. The geometric features of the iris can also be used for human identification. The structure of the iris is more related to the geometric shape and hence the extraction of these features is also possible. An overview of the performance metrics to evaluate a biometric system is also presented.

scholarly journals Identifikasi Iris Mata Menggunakan Metode Wavelet Daubechies dan K-Nearest Neighbor

2020 ◽  
Vol 2 (1) ◽  
pp. 1-10
Author(s):  
Fiena Efliana Alfian ◽  
I Gede Pasek Suta Wijaya ◽  
Fitri Bimantoro
Keyword(s):  
Wavelet Transform ◽  
Texture Analysis ◽  
Nearest Neighbor ◽  
Image Texture ◽  
Daubechies Wavelet ◽  
Analysis Method ◽  
K Nearest Neighbor ◽  
Iris Image ◽  
Human Iris ◽  
Level 1

Human iris has a very unique pattern which is different for each person so it is possible to use it as a basic of biometric recognition. To identify texture in an image, texture analysis method can be used. There is some texture analysis method, one of them is wavelet that extract the feature of image based on energy. In this research made a simulation to identified eyes iris based on Daubechies wavelet transform. First, the image of iris is segmented from eye image then enhanced with histogram equalization. Then used Daubechies wavelet method to get the energy value. The next step is recognition using K-Nearest Neighbor as the data classification. Three experiments are done in the research, those are influence of number of samples in database, influence of Daubechies wavelet transform level, and influence of the number of testing samples to calculate the level of False Positive Rate. As the result, the highest accuracy is achieved using Daubechies 8 level 3 with three samples iris image saved is 93,50%. Then, the lowest accuracy is achieved using Daubechies 4 level 1 and 3, and Daubechies 6 level 1 with one sample iris image saved is 91,50%. Keywords: biometric, human iris, texture analysis, Daubechies wavelet transform, K-Nearest Neighbor

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