scholarly journals Biometric Private Iris Recognition from an Image at Long Distance

2019 ◽  
Vol 8 (5S3) ◽  
pp. 539-550
Keyword(s):  
Iris Recognition ◽  
Near Infrared ◽  
State Of The Art ◽  
Recognition System ◽  
Biometric System ◽  
Indian Government ◽  
Long Distance ◽  
Effective Technique ◽  
Recognition Systems ◽  
The Individual

The specially finished annular element of the person eye which is remotely visible is - iris. This iris recognition is useful to identify the individual. In number of applications the iris recognition system is used. Most of the countries uses biometric system for security purpose such that in airfield boarding, custom clearance, congregation entrance and so on. The Indian government also uses biometric system for identification of citizen in different applications like as in rashan shop, Aadhar project, in different government exam forms and registration dept. etc. The customary iris recognition systems develop near infrared (NIR) sensors to obtain pictures of the iris. But in this method the iris can acquire distance less than 1 meter. In the course of the last several years, there have been different designs to plan and complete iris acknowledgment frameworks which operates at longer distance ranging from 1 meter to 60 meter. Therefore, due to such long range of iris recognition systems and iris acquisition system gives to the best applications to the client. In this paper, an effective technique for iris recognition is present to identify the individual. It uses iris-recognition-at-a-distance (IAAD) system and state-of-the-art design methods to audits the iris recognition system. The primary point of this article is analyzing the criticalness and employments of IAAD systems with respect to human recognition, the review of existing IAAD structures, comparison of different method which are already implemented in literature and improvement of IAAD accuracy along with iris.

Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages

2021 ◽  
Vol 20 (4) ◽  
pp. 1-19
Author(s):  
Manjunath K. E. ◽  
Srinivasa Raghavan K. M. ◽  
K. Sreenivasa Rao ◽  
Dinesh Babu Jayagopi ◽  
V. Ramasubramanian
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Window Size ◽  
Recognition System ◽  
Error Rates ◽  
Indian Languages ◽  
International Phonetic Alphabet ◽  
Phone Recognition ◽  
Front End ◽  
Recognition Systems

In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (LID-Mono), trained individually on each of the languages present in multilingual dataset. In the second approach, a common multilingual phone-set derived from the International Phonetic Alphabet (IPA) transcription of the multilingual dataset is used to develop a Multilingual Phone Recognition System (Multi-PRS). The bilingual code-switching experiments are conducted using Kannada and Urdu languages. In the first approach, LID is performed using the state-of-the-art i-vectors. Both monolingual and multilingual phone recognition systems are trained using Deep Neural Networks. The performance of LID-Mono and Multi-PRS approaches are compared and analysed in detail. It is found that the performance of Multi-PRS approach is superior compared to more conventional LID-Mono approach in both code-switched and non-code-switched scenarios. For code-switched speech, the effect of length of segments (that are used to perform LID) on the performance of LID-Mono system is studied by varying the window size from 500 ms to 5.0 s, and full utterance. The LID-Mono approach heavily depends on the accuracy of the LID system and the LID errors cannot be recovered. But, the Multi-PRS system by virtue of not having to do a front-end LID switching and designed based on the common multilingual phone-set derived from several languages, is not constrained by the accuracy of the LID system, and hence performs effectively on code-switched and non-code-switched speech, offering low Phone Error Rates than the LID-Mono system.

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.

scholarly journals A Multiscale Chaotic Feature Extraction Method for Speaker Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiang Lin ◽  
Yi Yumei ◽  
Zhang Maosheng ◽  
Chen Defeng ◽  
Wang Chao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Extraction Method ◽  
State Of The Art ◽  
Recognition System ◽  
Nonlinear Dynamic Model ◽  
Feature Extraction Method ◽  
Analysis Technique ◽  
Recognition Systems ◽  
Environment Noise

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively.

scholarly journals Design of a Face Recognition System based on Convolutional Neural Network (CNN)

2020 ◽  
Vol 10 (3) ◽  
pp. 5608-5612 ◽  
Author(s):  
Y. Said ◽  
M. Barr ◽  
H. E. Ahmed
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Great Variability ◽  
Artificial Vision ◽  
Surveillance Systems ◽  
Distributed Network ◽  
Biometric System ◽  
Face Recognition System ◽  
Deep Learning Model

Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

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. 

A REVIEW OF IRIS RECOGNITION SYSTEM

2015 ◽  
Vol 77 (1) ◽  
Author(s):  
Arezou Banitalebi Dehkordi ◽  
Syed A. R. Abu-Bakar
Keyword(s):  
Feature Extraction ◽  
Image Compression ◽  
Quality Assessment ◽  
Noise Reduction ◽  
Iris Recognition ◽  
Image Acquisition ◽  
Recognition System ◽  
Biometric System ◽  
Large Database ◽  
Database Applications

Iris recognition system is an accurate biometric system. In recent years, iris recognition is developed to several active areas of research, such as; Image Acquisition, restoration, quality assessment, image compression, segmentation, noise reduction, normalization, feature extraction, iris code matching, searching large database, applications, evaluation, performance under varying condition and multibiometrics. This paper reviews a background of iris recognition and literature of recent proposed methods in different fields of iris recognition system from 2007 to 2015.

A Review on Biometric Technology

2018 ◽  
Vol 8 (4) ◽  
pp. 101
Author(s):  
Kartik Choudhary ◽  
Rizwan Khan
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Behavioral Characteristics ◽  
Biometric System ◽  
Biometric Technology ◽  
The Face ◽  
The Individual ◽  
Recognition Technique ◽  
Better Than

Biometric Technology has turned out to be a popular area of research in computer vision and one of the most successful applications for identifying humans by capturing and analysing the sole feature or characteristic of   individual which is possessed by them and involves their Physical and Behavioral characteristics. For the individual validation and authentication the biometric system has this responsibility. Biometric Technology started from the fingerprints recognition and later on improvements were done in it to make it more secure which involves the face recognition and iris Recognition. Almost both of them are available and regarded as the accurate and reliable technology for biometric validation system. This review paper is all about Face recognition techniques in biometric locking system and Iris recognition technique of identification and the ways of making locking systems ways more efficient, full of ease, more secure, and far better than before so as to make locking or security stronger. It discusses about face recognition technique, its working and its application in different sector along with iris recognition, its working, its application.

Adoption of Dual Iris and Periocular Recognition for Human Identification

2016 ◽  
pp. 250-266
Author(s):  
R. Deepika ◽  
M. R. Prasad ◽  
Srinivas Chetana ◽  
T. C. Manjunath
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Personal Identification ◽  
Iris Segmentation ◽  
Biometric System ◽  
Multiple Features ◽  
Facial Region ◽  
Multimodal Biometric System ◽  
Segmentation Approach ◽  
Periocular Recognition

Personal identification from the iris images acquired under less-constrained imaging environment is highly challenging. Such environment requires the development of efficient iris segmentation approach and recognition strategy which can exploit multiple features available for the potential identification. So, along with the iris features periocular features have increasing attention in biometrics technology. For the recognition purpose iris and periocular information are collected from both the eyes of same person simultaneously. The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of image for periocular biometric is expected to require less subject cooperation. In this chapter, a dual iris based multimodal biometric system that increases the performance and accuracy of the typical iris recognition system is proposed.

scholarly journals Extraction of Iris Crypt, Pigment Spot, and Wolfflin Nodule Biological Feature using Feature Point Selection Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 2225-2230
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Feature Points ◽  
Biological Feature ◽  
Point Selection ◽  
Human Eye ◽  
Biological Features ◽  
Human Iris ◽  
The Individual ◽  
Selection Algorithms

The iris biometrics is an important biological feature of the human. The iris is the part of human eye. The human eye consists of many features. Iris is one of the unique features of human eye. In this paper we propose an algorithm to extract the features of iris. The existing algorithms are based on combined biological features of iris. We are going to introduce separate biological features and extract them one by one using suitable algorithms. The proposed method is used to extract the biological features of human iris. The proposed method uses crypts, pigment layers, and Wolfflin nodules features of iris. Each feature is extracted initially and suitable feature selection algorithm is identified. The manual cropping is initially applied in the eye image which extracts iris layer. The manual cropping is further applied on iris to locate the biological layers. Canny edge detection is applied on each iris feature. The FAST, SURF, MinEigen, BRISK, and MSER feature points are collected from each biological layer. The MinEigen extracts 218 feature points from the crypt layer. The BRISK extracts 161 and 89 feature points from the pigment and Wolfflin nodules. The proposed system can be used in iris recognition system all over the world for various authentication and security purposes. The individual feature extraction helps to make the recognition system more secure and accurate as compared to the existing systems which uses combined biological features. Thus, the proposed system is advantageous as compared to the existing systems and the efficiency will also be high if it is used for iris recognition.

scholarly journals EAgeBioS: Enhanced Biometric System to handle the Effects of Template Aging

2019 ◽  
Vol 9 (1) ◽  
pp. 3669-3677
Keyword(s):  
Error Rate ◽  
Iris Recognition ◽  
Rejection Rate ◽  
Recognition System ◽  
Biometric System ◽  
Security Systems ◽  
False Rejection Rate ◽  
Biometric Systems ◽  
Template Aging ◽  
False Rejection

Biometric Systems are well-known security systems that can be used anywhere for authentication, authorization or any kind of security verifications. In biometric systems, the samples are trained first and then it can be used for testing in long runs. Many recent researches have shown that a biometric system may fail or get compromised because of the aging of the biometric templates. The fact that temporal duration affects the performance of the biometric system has shattered the belief that iris does not change over lifetime. This is also possible in the case of iris. So, the main focus of this work is to analyze the effect of aging and also to propose a new system that can deal with template aging. We have proposed a new iris recognition system with an image enhancement mechanism and different feature extraction mechanisms. In this work, three different features are extracted, which are then fused to be used as one. The full system is trained on a dataset of 2500 samples for the year 2008 and testing is done in three different phases (i) No-Lapse, (ii) 1-Year Lapse and (iii) 2-Year Lapse. A portion of the ND-Iris-Template-Aging dataset [11] is used with a period of three years lapse. Results show that the performance of the hybrid classifier AHyBrK [17] is improved as compared to KNN and ANN and the effect of aging in terms of degraded performance is clear. The performance of this system is measured in terms of False Rejection Rate, Error Rate, and Accuracy. The overall performance of AHyBrK is 51.04% and 52.98% better than KNN and ANN respectively in terms of False Rejection Rate and Error Rate whereas the accuracy of this proposed system is also improved by 5.52% and 6.04% as compared to KNN and ANN respectively. This proposed system also achieved high accuracy for all the test phases.

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