Development of a Enhanced Ear Recognition System for Personal Identification

2019 ◽  
Author(s):  
Bassam S. Ali ◽  
Osman Nuri Ucan ◽  
Oguz BAYAT
Keyword(s):  
Recognition System ◽  
Personal Identification ◽  
Ear Recognition

Human Ear Recognition System

2015 ◽  
pp. 5475-5481
Author(s):  
Durgesh Singh ◽  
Sanjay Kumar Singh
Keyword(s):  
Recognition System ◽  
Personal Identification ◽  
Law Enforcement Agencies ◽  
Ear Recognition ◽  
Knowledge Based ◽  
Ear Detection ◽  
Biometric Systems ◽  
Identity Based ◽  
Human Ear ◽  
Traditional Approaches

A reliable human recognition scheme is required in wide variety of systems to either verify or identify the identity of an individual requesting their services. Using traditional approaches such as possession based and knowledge based systems, it is very difficult to differentiate between an authorized person and an impostor. This is a strong reason for replacing traditional ID-based systems with biometric systems which are based on human traits that cannot be denied, stolen, or faked easily. Biometric recognition refers to the automatic recognition, based on physiological and /or behavioral characteristics of an individual. By using biometrics, it is possible to establish an individual's identity based on “who he or she is” rather than by “what he or she possesses likes smart card” or “what he or she remembers likes password.” Human ear due to its consistent behavior over the age, has gained much popularity in recent years among various physiological biometric traits. The decidability index of the ear has been found that magnitude significant greater than that of face. Ear remarkably consistent and does not change its shape under expressions like face. The shape of the outer ear is recognized as a valuable means for personal identification. Naturally, an ear biometric system consists of ear detection and ear recognition modules. Ear biometric has played an important role for many years in forensic science and its use by law enforcement agencies.

ALGORITHMS FOR FINGERPRINT CLASSIFICATION

2021 ◽  
Vol 335 (1) ◽  
pp. 39-44
Author(s):  
D. Lebedev ◽  
A. Abzhalilova
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
External Object ◽  
Biometric Identification ◽  
Fingerprint Classification ◽  
Combined Application ◽  
Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

scholarly journals CAPSULE NETWORK BASED BIOMETRIC RECOGNITION SYSTEM

2019 ◽  
Vol 2019 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Dr. I. Jeena Jacob
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Noise Removal ◽  
Personal Identification ◽  
Biometric Recognition ◽  
Learning Techniques ◽  
Frame Work ◽  
The Stability

The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

An Efficient 3-D Ear Recognition System Employing Local and Holistic Features

2012 ◽  
Vol 7 (3) ◽  
pp. 978-991 ◽  
Author(s):  
Jindan Zhou ◽  
Steven Cadavid ◽  
Mohamed Abdel-Mottaleb
Keyword(s):  
Recognition System ◽  
Ear Recognition

Research and Evaluation of Multispectral Feature Selection and Fusion Model for Palmprint Recognition

2013 ◽  
Vol 411-414 ◽  
pp. 1291-1294
Author(s):  
Ying Xu ◽  
Fei Luo
Keyword(s):  
Bp Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Singular Value ◽  
Personal Identification ◽  
Palmprint Recognition ◽  
Fusion Model ◽  
Network Method ◽  
Robust Recognition ◽  
Value Decomposition

Palmprint is widely used in personal identification for an accurate and robust recognition. Multispectral palmprint images capture under different illumination, including Red, Green, Blue and Infrared maybe contribute to the recognition results. However, the evaluation of selection and fusion of how this different spectral images can contribute to improve the robustness of the recognition system is imperative. In this paper, a novel wavelet-based multispectral fusion strategy is presented firstly to obtain the fused images; then block singular value decomposition (B-SVD) is applied for feature extraction; Finally back propagation (BP) neural network method is adopted for authentication. The proposed algorithm is evaluated on PolyU database which contains palmprint images from 500 individuals from four independent frequent band. The obtained results show robustness of our multispectral palmprint image fusion and selection model in comparison with the single spectral palmprint image that presented in the literature.

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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