Human Ear Recognition System

Author(s):  
Durgesh Singh ◽  
Sanjay Kumar Singh

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.

Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar

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.


Author(s):  
D. Lebedev ◽  
A. Abzhalilova

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.


2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Li Yuan ◽  
Zhichun Mu

We propose an ear recognition system based on 2D ear images which includes three stages: ear enrollment, feature extraction, and ear recognition. Ear enrollment includes ear detection and ear normalization. The ear detection approach based on improved Adaboost algorithm detects the ear part under complex background using two steps: offline cascaded classifier training and online ear detection. Then Active Shape Model is applied to segment the ear part and normalize all the ear images to the same size. For its eminent characteristics in spatial local feature extraction and orientation selection, Gabor filter based ear feature extraction is presented in this paper. Kernel Fisher Discriminant Analysis (KFDA) is then applied for dimension reduction of the high-dimensional Gabor features. Finally distance based classifier is applied for ear recognition. Experimental results of ear recognition on two datasets (USTB and UND datasets) and the performance of the ear authentication system show the feasibility and effectiveness of the proposed approach.


Author(s):  
Abbas Hassin ◽  
Dheyaa Abbood

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.


2015 ◽  
Vol 1 (7) ◽  
pp. 283 ◽  
Author(s):  
Rubal Jain ◽  
Chander Kant

Biometrics is a pattern recognition system that refers to the use of different physiological (face, fingerprints, etc.) and behavioral (voice, gait etc.) traits for identification and verification purposes. A biometrics-based personal authentication system has numerous advantages over traditional systems such as token-based (e.g., ID cards) or knowledge-based (e.g., password) but they are at the risk of attacks. This paper presents a literature review of attack system architecture and makes progress towards various attack points in biometric system. These attacks may compromise the template resulting in reducing the security of the system and motivates to study existing biometric template protection techniques to resist these attacks.


Author(s):  
D. Lebedev ◽  
A. Abzhalilova

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.


Author(s):  
Mais Al-Sharqi ◽  
Haitham Sabah Hasan

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.


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