A Survey on Human Ear Recognition System Based on 2D and 3D Ear Images

2014 ◽  
Vol 2014 (2) ◽  
pp. 21-30 ◽  
Author(s):  
Durgesh Singh ◽  
◽  
Sanjay Singh ◽  
Keyword(s):  
Recognition System ◽  
Ear Recognition ◽  
Human Ear ◽  
2D And 3D

scholarly journals Human – Ear Recognition Using Scale Invariant Feature Transform

2021 ◽  
pp. 1-12
Author(s):  
Abbas Hassin ◽  
Dheyaa Abbood
Keyword(s):  
Distance Measure ◽  
Recognition System ◽  
Scale Invariant Feature Transform ◽  
Morphological Operations ◽  
Scale Invariant ◽  
Ear Recognition ◽  
Invariant Feature ◽  
Feature Transform ◽  
Human Ear ◽  
Scale Invariant Feature

Biometrics techniques are the standard of a wide group of many applications for a human’s identification and verification issues. Because of this reason, a high scale of security needs to search for a new way to identify the person arises. In this paper, establish a human ear recognition system is proposed. This system combines four main phases: ear detection, ear feature extraction, ear recognition, and confirmation. The essential of the proposed system is to divide the ear image into the skin and non-skin pixels using a likelihood skin detector. The likelihood image processes by morphological operations to complete ear regions.  Scale-invariant feature transform uses for extracting the fixed features of the ear. Ear recognition includes two modes identification mode and verification mode. Euclidean Distance Measure (EDM) uses for similarity measure between the first image in the database and a new image. According to the three experiments conducted in this paper, the results of the different datasets, the accuracy ratio are 100%, 92%.and 92% respectively.

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.

Development of a Modified Ear Recognition System for Personnel Identification

2020 ◽  
Vol 13 ◽  
Author(s):  
Mais Al-Sharqi ◽  
Haitham Sabah Hasan
Keyword(s):  
Recognition Accuracy ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Identification System ◽  
Segmentation Method ◽  
False Acceptance Rate ◽  
Ear Recognition ◽  
Human Ear ◽  
Algorithm Implementation

Aims: This study examined the development of a match region localization (MRL) ear recognition system (ERS). Background: The developed algorithm is called the match region localization (MRL) algorithm. MRL recognizes a human ear using only small visible portions of the ear while excluding covered or occluded portions. The MRL technique divides an ear image into segments of small blocks; these blocks are either regular (and equally sized) segments or irregularly shaped blocks depending on the adopted segmentation method. Objective: The recognition accuracy of the system is 97.07%, thereby implying that the system can perform efficiently as an identification system. Method: This research follows four major stages, namely, development of a PCA-based ear recognition algorithm, implementation of the developed algorithm, determination of the optimum ear segmentation method, and evaluation of the performance of the technique. Results: The False acceptance rate (FAR) of the developed ear recognition system (ERS) is 0.06. This result implies that six out of every 100 intruders will be falsely accepted. Conclusion: The developed ERS outperforms the existing ERS by approximately 24.61% in terms of system recognition accuracy; the developed ERS can be tested on other publicly available ear databases to check its performance on larger platforms. Other: The developed ERS can be tested on other publicly available ear databases to check its performance on larger platforms.

scholarly journals An ear recognition system based on local wavelet subband energy distribution

2021 ◽  
Vol 24 (2) ◽  
pp. 1212
Author(s):  
Ruaa Isam Fadhil ◽  
Loay E. George
Keyword(s):  
Energy Distribution ◽  
Recognition Rate ◽  
Low Frequency ◽  
Recognition System ◽  
Individual Identification ◽  
Full Description ◽  
Ear Recognition ◽  
Biometric Characteristic ◽  
Human Ear ◽  
Selection Of

The outer ear features have been used for many years in forensic science of recognition. Human ear is a valuable information provenance of data for individual identification/authentication. Ear meets biometric characteristic (universality, distinctiveness, permanence and collectability). Biometric system depending on ear image facing two major challenges; the first one is the localization of human ear area in given profile face image, and the second one is the selection of proper features to distinguish between individuals. In this work, we propose an alogorithm for ear recognition based on the local spatial energy distribution of wavelet sub-bands, because of wavelet transform has the ability to analyze the local feature of 2-D image by determining where the low frequency and high frequency areas are and it provides full description of the spatial distribution of the ear image. Nearest classifier are used to make a recognition decision in matching stage. The system was tested over a public database consist of 493 images. The attained recognition rate was (95.28%) and the achieved minimum equal error rate (EER) is 0.02%.

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

Combining Block DCV and Support Vector Machine for Ear Recognition

2016 ◽  
Vol 8 (2) ◽  
pp. 36-44 ◽  
Author(s):  
Zhao Hailong ◽  
Yi Junyan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Construction Method ◽  
Experimental Results ◽  
Support Vector ◽  
Recognition Method ◽  
Ear Recognition ◽  
Human Ear ◽  
Better Than

In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.

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