scholarly journals Biometric Iris Recognition System using Multiscale Feature Extraction Method

2020 ◽  
Vol 8 (6) ◽  
pp. 2298-2303
Keyword(s):  
Iris Recognition ◽  
Computation Time ◽  
Principal Component ◽  
Recognition System ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Biometric Recognition ◽  
Feature Extraction Method ◽  
Research And Education ◽  
Hand Geometry

By an growing demand for security systems, identification of individuals based on biometric techniques has been a major role of research and education. Biometric recognition examines unique behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry etc. The iris is one of the highly consistentmethods that used to identify individuals because it is fixed and does not change throughout life. This features have led to increasing importance in its use for biometric recognition. In this study, we proposed a system combiningDiscrete Wavelet Transformation and Principal Component Analysis forfeature extraction process of an iris. The idea of using DWT behind PCA is to decrease the resolution of the iris pattern. The Discrete Wavelet Transform (DWT) is depend on sub-band codingwhichreduces the computation time and resources required. PCA is used for further extraction. Our experimental calculation supports the efficient performance of the proposed system.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals A fast Iris recognition system through optimum feature extraction

2018 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person's lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris templates classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals A fast iris recognition system through optimum feature extraction

2019 ◽  
Vol 5 ◽  
pp. e184 ◽  
Author(s):  
Humayan Kabir Rana ◽  
Md. Shafiul Azam ◽  
Mst. Rashida Akhtar ◽  
Julian M.W. Quinn ◽  
Mohammad Ali Moni
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Discrete Wavelet ◽  
Automated Identification ◽  
Biometric Recognition ◽  
Efficient Performance ◽  
Increasing Demand ◽  
Hand Geometry

With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

scholarly journals Iris Biometric Modality: A Review

2017 ◽  
Vol 10 (2) ◽  
pp. 502-506 ◽  
Author(s):  
Iqra Mattoo ◽  
Parul Agarwal
Keyword(s):  
Face Recognition ◽  
Iris Recognition ◽  
Review Paper ◽  
Voice Recognition ◽  
Recognition System ◽  
Physiological Traits ◽  
Robust Method ◽  
Patient Identification ◽  
Biometric Recognition ◽  
Hand Geometry

Biometric Recognition is the most suitable and informed identification method which is used in different fields due to its uniqueness of the countless behavioural and physiological traits like hand geometry, finger prints, iris recognition, face recognition, handwriting, voice recognition, etc. Iris recognition system is widely being used as it has inherently distinctive patterns that provide a robust method for the identification purpose. Different nations have already started to use biometric recognition system for the identification purposes including patient identification, border security, etc. In this review paper, different steps that are involved in Iris Recognition system are defined and evaluation of different Iris Recognition methods used by different researchers for each recognition step is done as well.

scholarly journals Iris Matching Step Implementation in FPGA

2019 ◽  
Vol 2 (1) ◽  
pp. 26-36
Author(s):  
Aumama M. Farhan ◽  
M. F. Al-Gailani
Keyword(s):  
Execution Time ◽  
Iris Recognition ◽  
Hardware Implementation ◽  
Hamming Distance ◽  
Computation Time ◽  
Recognition System ◽  
Hardware Platform ◽  
System Generator ◽  
Large Memory ◽  
Powerful Strategy

Iris recognition system is broadly being utilized as it has distinctive patterns that gives it a powerful strategy to distinguish between persons for identification purposes. However, this system in this implementation requires large memory capacity and high computation time. These factors make us in a challenge to find a way to run this algorithm in a hardware platform. The hardware implementation features reduce the execution time by exploiting the parallelism and pipeline. The present work addresses this issue when reducing execution time by implementing the matching step using hamming distance algorithm on the target device FPGA KINTEX 7 using Xilinx system generator. The obtained result demonstrates that the execution time has been accelerated to 1.32 ns, which is almost at least four times faster than existing works

scholarly journals Face Recognition System for Indoor Applications Based on Video with the LNMF and NMFsc Methods

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Della Gressinda Wahana ◽  
Bambang Hidayat ◽  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Face Detection ◽  
Matrix Factorization ◽  
Computation Time ◽  
Principal Component ◽  
Recognition System ◽  
Face Recognition System ◽  
Average Accuracy ◽  
Non Negative Matrix Factorization

Face detection and face recognition are among the most important research topics in computer vision, as many applications use faces as objects of biometric technology. One of the main issues in applying face recognition is recording the attendance of active participants in a room. The challenge is that recognition through video with multiple object conditions in one frame may be difficult to perform. The Principal Component Analysis method is commonly used in face detection. Principal Component Analysis still has shortcomings: the accuracy decreases when it is applied to large datasets and performs slowly in real-time applications. Therefore, this study simulates a face recognition system installed in a room based on video recordings using Non-negative Matrix Factorization suppressed carrier and Local Non-negative Matrix Factorization methods. Data acquisition is obtained by capturing video in classrooms with a resolution of 640 x 480 pixels in RGB, .avi format, video frame rate of 30 fps, and video duration of ±10 seconds. The proposed system can perform face recognition in which the average accuracy value of the Local Non-negative Matrix Factorization method is 71.61% with a computation time of 152,634 seconds. By contrast, the average accuracy value of the Non-negative Matrix Factorization suppressed carrier method is 86.76% with a computation time of 467,785 seconds. The proposed system is expected to be used for simultaneously finding and identifying faces in real time.

scholarly journals Biometric Recognition of Finger Knuckle Print Based on the Fusion of Global Features and Local Features

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Wenwen Li
Keyword(s):  
Recognition Rate ◽  
Principal Component ◽  
Texture Features ◽  
Single Mode ◽  
Recognition System ◽  
Local Features ◽  
Identification System ◽  
Biometric Recognition ◽  
Global Features ◽  
Hand Shape

Compared with the most traditional fingerprint identification, knuckle print and hand shape are more stable, not easy to abrase, forge, and pilfer; in aspect of image acquisition, the requirement of acquisition equipment and environment are not high; and the noncontact acquisition method also greatly improves the users’ satisfaction; therefore, finger knuckle print and hand shape of single-mode identification system have attracted extensive attention both at home and abroad. A large number of studies show that multibiometric fusion can greatly improve the recognition rate, antiattack, and robustness of the biometric recognition system. A method combining global features and local features was designed for the recognition of finger knuckle print images. On the one hand, principal component analysis (PCA) was used as the global feature for rapid recognition. On the other hand, the local binary pattern (LBP) operator was taken as the local feature in order to extract the texture features that can reflect details. A two-layer serial fusion strategy is proposed in the combination of global and local features. Firstly, the sample library scope was narrowed according to the global matching result. Secondly, the matching result was further determined by fine matching. By combining the fast speed of global coarse matching and the high accuracy of local refined matching, the designed method can improve the recognition rate and the recognition speed.

Sign in / Sign up

Export Citation Format

Share Document