A new approach to biometric recognition based on hand geometry

2015 ◽  
Author(s):  
Márcia V. P. do Nascimento ◽  
Leonardo V. Batista ◽  
N. L. Cavalcanti
Keyword(s):  
Biometric Recognition ◽  
New Approach ◽  
Hand Geometry

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 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

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 NEW APPROACH TOWARDS EFFICIENT BIOMETRIC SYSTEMS

2011 ◽  
pp. 108-113
Author(s):  
Chander Kant
Keyword(s):  
Recognition System ◽  
Fingerprint Matching ◽  
Biometric System ◽  
Biometric Recognition ◽  
New Approach ◽  
Matching Process ◽  
Biometric Template ◽  
Right Loop ◽  
Biometric Systems ◽  
Special Pattern

Fingerprints possess two main types of features that are used for automatic fingerprint identification and verification: (i) Ridge and Furrow structure that forms a special pattern in the central region of the fingerprint and (ii) Minutiae details associated with the local ridge and furrow structure. In a traditional biometric recognition system, the biometric template is usually stored on a central server during enrollment. The candidate biometric template captured by the biometric device is sent to the server where the processing and matching steps are performed. The proposed work presents an approach to the processing time during fingerprint matching process in a Biometric System. The proposed work is based upon four major classifications of fingerprint, whorl, arch, left-loop and right-loop and is more efficient as compared with the existing 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.

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