scholarly journals Performance Analysis of MSB Based Iris Recognition Using Hybrid Features Extraction Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 230-239 ◽  
Keyword(s):  
Euclidean Distance ◽  
Iris Recognition ◽  
Recognition Accuracy ◽  
Image Features ◽  
Discrete Wavelet ◽  
Morphological Operations ◽  
Physiological Trait ◽  
Human Identity ◽  
Hybrid Features ◽  
Left And Right

In the modern days, biometric identification is more promising and reliable to verify the human identity. Biometric refers to a science for analyzing the human characteristics such as physiological or behavioral patterns. Iris is a physiological trait, which is unique among all the biometric traits to recognize an individual effectively. In this paper, MSB based iris recognition based on Discrete Wavelet Transform, Independent Component Analysis and Binariezed Statistical Image Features is proposed. The left and right region is extracted from eye images using morphological operations. Binary split is performed to divide the eight-bit binary of every pixel into four bit Least Significant Bits and four bit Most Significant Bits. DWT is applied on four bit MSB to extract the iris features. Then ICA is applied on approximate sub band to extract the significant details of iris. The obtained features are then applied on BSIF to obtain the enhanced response with final features. Finally features produced are matched with the test features using Euclidean distance classifier on CASIA database. The experiments are performed on proposed iris model using MATLAB 7.0 software considering various combinations of Person inside Database (PID’s) and Person outside Database (POD’s) to evaluate the recognition accuracy of the proposed iris model.

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 RECOGNITION BASED ON THE MODIFIED CHAN-VESE ACTIVE CONTOUR

2016 ◽  
Vol 78 (10-3) ◽  
Author(s):  
Shahrizan Jamaludin ◽  
Nasharuddin Zainal ◽  
W Mimi Diyana W Zaki
Keyword(s):  
Differential Operator ◽  
Execution Time ◽  
Active Contour ◽  
Area Under Curve ◽  
Iris Recognition ◽  
Recognition Accuracy ◽  
Operator Method ◽  
High Accuracy ◽  
Biometric System ◽  
Human Identity

Over recent years, iris recognition has been an explosive growth of interest in human identification due to its high accuracy. Iris recognition is a biometric system that uses iris to verify and identify human identity. Iris has pattern that is rich with textures and can be compared among humans. There are many methods can be used in iris recognition. The methods based on the integro-differential operator and Hough transform are the most widely used in iris recognition. Unfortunately, both methods require more time to execute and has less accurate recognition accuracy due to the eyelid occlusion. In order to solve these problems, the Chan-Vese active contour is modified to reduce the execution time and to increase the recognition accuracy of iris recognition. Then, this method is compared with the integro-differential operator method. The iris images from CASIA-v4 database are used for the experiments. According to the results, the proposed method recorded 0.91 s for execution time which was 61.28 % faster than the integro-differential operator method. The proposed method also achieved 0.9831 for area under curve (AUC) which was 2.66 % higher recognition accuracy than the integro-differential operator method. To conclude, the modified Chan-Vese active contour was able to improve the performance of iris recognition compared to the integro-differential operator method. 

Circular Construction of Iris Feature Vectors for Human Iris Recognition Systems

2013 ◽  
Vol 330 ◽  
pp. 991-995
Author(s):  
Li Na ◽  
Danu Widatama ◽  
Tony Mulia ◽  
Benyamin Kusumoputro
Keyword(s):  
Conventional Method ◽  
Euclidean Distance ◽  
Iris Recognition ◽  
Feature Vector ◽  
Recognition Accuracy ◽  
Distance Method ◽  
Pixel Intensity ◽  
Feature Vectors ◽  
Human Iris ◽  
Recognition Systems

In this paper, the authors propose a new method to construct feature vectors of human iris based on circular technique. Since an iris has a circular shape, the circular construction of the iris feature vector is expected to have higher ability to capture iris characteristics. Different from the conventional method which constructs iris feature vectors through left to right scanning process, the circular technique scans the pixel-intensity value of iris circularly. As the result, the length of the constructed feature vectors is dependent on the length of iris radius. In the experiments, various recognition methods of feature vectors were compared. The iris recognition results show that the proposed system with Statistical Euclidean Distance method gives the highest recognition accuracy with 84.5%.

scholarly journals SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis

2017 ◽  
Vol 7 (5) ◽  
pp. 2555 ◽  
Author(s):  
Madina Hamiane ◽  
Fatema Saeed
Keyword(s):  
Wavelet Transforms ◽  
Noise Removal ◽  
Image Features ◽  
Support Vector ◽  
Computer Assisted ◽  
Discrete Wavelet ◽  
Normal Brain ◽  
Morphological Operations ◽  
Malignant Tumours ◽  
Brain Images

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature.

scholarly journals Enhanced Iris Recognition for Person Identification

2019 ◽  
Vol 8 (4) ◽  
pp. 7400-7404
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Euclidean Distance ◽  
Iris Recognition ◽  
Feature Matching ◽  
Recognition Task ◽  
Personal Identification ◽  
Discrete Wavelet ◽  
Person Identification

Iris recognition is a secure biometric for personal identification. Commonly used biometric are voice, face, fingerprint, iris etc. Among this iris recognition is considered as more accurate, because iris is externally visible and the texture patterns are unique and stable throughout a person’s whole life. The main steps involved in iris recognition are pre-processing, feature extraction and feature matching. Unique preprocessing methods are mentioned in this work. The feature extraction phase is imperative in iris recognition task. Here in this work feature extraction utilizing Discrete Wavelet Transform (DWT) and matching of iris pictures utilizing Euclidean distance

scholarly journals Biometric Identification using Human Ear

2019 ◽  
Vol 9 (1) ◽  
pp. 4893-4898
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Euclidean Distance ◽  
Iris Recognition ◽  
Biometric Identification ◽  
Discrete Wavelet ◽  
Grayscale Image ◽  
Test Image ◽  
Human Ear ◽  
Over Time

Biometrics refers to the metrics of the human characteristics which has gained much popularity in recent times. It is a form of identification and access control. Widely used forms of biometrics are facial recognition, finger print recognition, iris recognition, etc. but the drawback is that most of these features change over time. The human ear is a cogent source of data to classify biometrically since its attributes do not change substantially as time progresses. This paper explores the field of ear biometric wherein the database images are re-sized to 128 x 256 pixels and then converted to grayscale image. Various transforms viz. Discrete Cosine Transform, Discrete Fourier Transform, Discrete Wavelet Transform are then applied to extract the features. The coefficients of the test image are compared with the coefficients of the registered database image. On comparison, Euclidean distance classifier is used to recognize the test image from the database. The database used consists of 25 subjects with 6 images per person out of which the initial 4 images are used to train the model, and the remaining 2 for testing. The outputs of various transforms were compared and the best accuracy obtained is 86% using Discrete Wavelet Transform.

scholarly journals Perbandingan DTCWT dan NMF pada Face Recognition menggunakan Euclidean Distance

2014 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
David David ◽  
Ferdinand Ariandy Luwinda
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Image Features ◽  
Face Image ◽  
Experimental Result ◽  
High Dimensional ◽  
Discrete Wavelet ◽  
Directional Selectivity ◽  
Complex Wavelet Transform ◽  
Non Negative Matrix Factorization

Dual tree complex wavelet transform (DTCWT) is widely used for representation of face image features. DTCWT is more frequently used than Gabor or Discrete Wavelet Transform (DWT) because it provides good directional selectivity in six different directions. Meanwhile, non-negative matrix factorization (NMF) is also frequently used since it can reduce high dimensional feature into smaller one without losing important features. This research focused on comparison between DTCWT and NMF as feature extraction and Euclidean Distance for classification. This research used ORL Faces database. Experimental result showed that NMF provided better results than DTCWT did. NMF reached 92% of accuracy and DTCWT reached 78% of accuracy. 

scholarly journals Personal identity verification based ECG biometric using non-fiducial features

2020 ◽  
Vol 10 (3) ◽  
pp. 3007
Author(s):  
Marwa A. Elshahed
Keyword(s):  
Wavelet Decomposition ◽  
Euclidean Distance ◽  
Extraction Methods ◽  
Discrete Wavelet ◽  
Biometric System ◽  
Human Identity ◽  
Identity Verification ◽  
Discrete Wavelet Decomposition ◽  
Electrocardiogram Ecg ◽  
Number Of Individuals

Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

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