scholarly journals Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

2019 ◽  
Vol 8 (3) ◽  
pp. 2761-2767
Keyword(s):  
Iris Recognition ◽  
Gabor Filter ◽  
Feature Space ◽  
Recognition System ◽  
Feature Reduction ◽  
High Dimensional ◽  
Reduction Scheme ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Level Fusion

Iris recognition system has gained prominent focus because of its uniqueness, stability over time. But the recognition level of single biometric based recognition systems is greatly affected by environmental conditions, physiological deficiency. Multi-biometric systems diminish this problem with the fusion of features collected from various traits or samples of the same trait, a single trait by employing multiple algorithms or multiple instances. To gain the advantages of multi-biometric systems in iris recognition, a Multi-algorithmic iris recognition system has been proposed where Texture features from iris are extracted by using 2D-Log Gabor filter and Phase features are extracted by Haar Wavelet; And these features can be integrated at various levels like Decision, Rank, Score, feature, and pixel. Even though the feature level fusion contains rich information about biometric samples when compared to remaining fusion levels; it involves mapping complexity, high dimensional feature space. To gain advantage of feature level fusion in iris recognition and to overcome the problem of resulted high dimensional feature space, Genetic Algorithm (GA) based reduction scheme, Principal Component Analysis (PCA) reduction strategy and a hybrid reduction scheme which is a combination of PCA and GA have been applied to reduce the resulted feature space. The performance of these reduction strategies have evaluated on CASIA iris database, IIT Delhi iris database using Machine Learning approaches. The results have shown that the feature space has dramatically reduced while keeping recognition accuracy and also revealed that space and time requirements have significantly decreased after employing feature reduction schemes.

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammadreza Azimi ◽  
Seyed Ahmad Rasoulinejad ◽  
Andrzej Pacut
Keyword(s):  
Iris Recognition ◽  
Recognition Task ◽  
Aging Effect ◽  
Recognition System ◽  
Health Condition ◽  
Age Dependency ◽  
Biometric Systems ◽  
Kolmogorov Smirnov ◽  
Recognition Systems ◽  
Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

CLASSIFIER COMBINATION AND ITS APPLICATION IN IRIS RECOGNITION

2008 ◽  
Vol 22 (03) ◽  
pp. 617-638 ◽  
Author(s):  
XINHUA FENG ◽  
XIAOQING DING ◽  
YOUSHOU WU ◽  
PATRICK S. P. WANG
Keyword(s):  
Iris Recognition ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition System ◽  
Classifier Combination ◽  
Practical Application ◽  
Finite Samples ◽  
Biometric Systems ◽  
Matching Score ◽  
Combined Classifier

Classifier combination is an effective method to improve the recognition accuracy of a biometric system. It has been applied to many practical biometric systems and achieved excellent performance. However, there is little literature involving theoretical analysis on the effectiveness of classifier combination. In this paper, we investigate classifiers combined with the max and min rules. In particular, we compute the recognition performance of each combined classifier, and illustrate the condition in which the combined classifier outperforms the original unimodal classifier. We focus our study on personal verification, where the input pattern is classified into one of two categories, the genuine or the impostor. For simplicity, we further assume that the matching score produced by the original classifier follows a normal distribution and the outputs of different classifiers are independent and identically distributed. Randomly-generated data are employed to test our conclusion. The influence of finite samples is explored at the same time. Moreover, an iris recognition system, which adopts multiple snapshots to identify a subject, is introduced as a practical application of the above discussions.

scholarly journals A Novel Algorithm for Feature Level Fusion Using SVM Classifier for Multibiometrics-Based Person Identification

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ujwalla Gawande ◽  
Mukesh Zaveri ◽  
Avichal Kapur
Keyword(s):  
Learning Algorithm ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Rejection Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Fusion Algorithm ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Level Fusion

Recent times witnessed many advancements in the field of biometric and ultimodal biometric fields. This is typically observed in the area, of security, privacy, and forensics. Even for the best of unimodal biometric systems, it is often not possible to achieve a higher recognition rate. Multimodal biometric systems overcome various limitations of unimodal biometric systems, such as nonuniversality, lower false acceptance, and higher genuine acceptance rates. More reliable recognition performance is achievable as multiple pieces of evidence of the same identity are available. The work presented in this paper is focused on multimodal biometric system using fingerprint and iris. Distinct textual features of the iris and fingerprint are extracted using the Haar wavelet-based technique. A novel feature level fusion algorithm is developed to combine these unimodal features using the Mahalanobis distance technique. A support-vector-machine-based learning algorithm is used to train the system using the feature extracted. The performance of the proposed algorithms is validated and compared with other algorithms using the CASIA iris database and real fingerprint database. From the simulation results, it is evident that our algorithm has higher recognition rate and very less false rejection rate compared to existing approaches.

scholarly journals A Review of Iris Recognition Algorithms

2017 ◽  
Vol 1 (4-2) ◽  
pp. 175
Author(s):  
Abdulrahman Aminu Ghali ◽  
Sapiee Jamel ◽  
Kamaruddin Malik Mohamad ◽  
Nasir Abubakar Yakub ◽  
Mustafa Mat Deris
Keyword(s):  
Iris Recognition ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
Border Control ◽  
Biometric System ◽  
Rubber Sheet ◽  
Automated Teller Machines ◽  
Recognition Algorithms

With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays. The system has been used for years in many commercial and government applications that allow access control in places such as office, laboratory, armoury, automated teller machines (ATMs), and border control in airport. The aim of the paper is to review iris recognition algorithms. Iris recognition system consists of four main stages which are segmentation, normalization, feature extraction and matching. Based on the findings, the Hough transform, rubber sheet model, wavelet, Gabor filter, and hamming distance are the most common used algorithms in iris recognition stages.  This shows that, the algorithms have the potential and capability to enhanced iris recognition system. 

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