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.

Age dependency of the diabetes effects on the iris recognition systems performance evaluation results

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohammadreza Azimi ◽  
Seyed Ahmad Rasoulinejad ◽  
Andrzej Pacut
Keyword(s):  
Iris Recognition ◽  
Recognition Task ◽  
Aging Effect ◽  
Recognition System ◽  
Health Condition ◽  
Age Dependency ◽  
Biometric Systems ◽  
Kolmogorov Smirnov ◽  
Recognition Systems ◽  
Smirnov Test

AbstractIn this paper, we attempt to answer the questions whether iris recognition task under the influence of diabetes would be more difficult and whether the effects of diabetes and individuals’ age are uncorrelated. We hypothesized that the health condition of volunteers plays an important role in the performance of the iris recognition system. To confirm the obtained results, we reported the distribution of usable area in each subgroup to have a more comprehensive analysis of diabetes effects. There is no conducted study to investigate for which age group (young or old) the diabetes effect is more acute on the biometric results. For this purpose, we created a new database containing 1,906 samples from 509 eyes. We applied the weighted adaptive Hough ellipsopolar transform technique and contrast-adjusted Hough transform for segmentation of iris texture, along with three different encoding algorithms. To test the hypothesis related to physiological aging effect, Welches’s t-test and Kolmogorov–Smirnov test have been used to study the age-dependency of diabetes mellitus influence on the reliability of our chosen iris recognition system. Our results give some general hints related to age effect on performance of biometric systems for people with diabetes.

Overview

2011 ◽  
pp. 1-23
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Pattern Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Biometric System ◽  
Biometric Recognition ◽  
Biometric Data ◽  
Pattern Recognition System ◽  
Biometric Systems ◽  
History Of

A biometric system can be regarded as a pattern recognition system. In this chapter, we discuss two advanced pattern recognition technologies for biometric recognition, biometric data discrimination and multi-biometrics, to enhance the recognition performance of biometric systems. In Section 1.1, we discuss the necessity, importance, and applications of biometric recognition technology. A brief introduction of main biometric recognition technologies are presented in Section 1.2. In Section 1.3, we describe two advanced biometric recognition technologies, biometric data discrimination and multi-biometric technologies. Section 1.4 outlines the history of related work and highlights the content of each chapter of this book.

A Structured Approach towards Robust Database Collection for Speaker Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 53 ◽  
Author(s):  
Pardeep Sangwan ◽  
Saurabh Bhardwaj
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Recognition System ◽  
Classification Methods ◽  
Extraction Techniques ◽  
Recognition Phase ◽  
Speech Database ◽  
Biometric Systems ◽  
Recognition Systems ◽  
Structured Approach

<p>Speaker recognition systems are classified according to their database, feature extraction techniques and classification methods. It is analyzed that there is a much need to work upon all the dimensions of forensic speaker recognition systems from the very beginning phase of database collection to recognition phase. The present work provides a structured approach towards developing a robust speech database collection for efficient speaker recognition system. The database required for both systems is entirely different. The databases for biometric systems are readily available while databases for forensic speaker recognition system are scarce. The paper also presents several databases available for speaker recognition systems.</p><p> </p>

scholarly journals Railway Access Control System using Face Recognition

2020 ◽  
Vol 9 (3) ◽  
pp. 444-448
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Modern World ◽  
Qr Code ◽  
Quick Response ◽  
Code System ◽  
Railway Station ◽  
Face Recognition System ◽  
Access Control System ◽  
Recognition Systems

Nowadays booking tickets and getting inside a railway station is adifficult task. Manual checking becomes a burden and time consuming. Also as everything is getting digitized in this modern world introduce face recognition and Quick Response (QR) code system for entry helps in passenger convenience.Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time.So this system focuses on passengers’ convenience through allowing them to book tickets online and by introducing face recognition system and QR code system for entry to a railway station.This system helps inidentifying people who try to travel without buying tickets and also helps toapprehend the blacklisted person which increases security in the railway station. Online booking is one of the convenient ways tobook the ticket. This system also provides the convenience to passenger by issuing the digital ticket in the form of QR code thus avoiding any fuss due to the loss of the physical ticket.

scholarly journals Carbohydrate-structure-dependent recognition of desialylated serum glycoproteins in the liver and leucocytes. Two complementary systems

1985 ◽  
Vol 227 (2) ◽  
pp. 345-354 ◽  
Author(s):  
K Bezouska ◽  
O Táborský ◽  
J Kubrycht ◽  
M Pospísil ◽  
J Kocourek
Keyword(s):  
Plasma Membranes ◽  
Recognition System ◽  
Carbohydrate Structure ◽  
Pathological Conditions ◽  
Different Types ◽  
Recognition Systems ◽  
Specificity Pattern ◽  
Apparent Association Constants ◽  
Reversed Order ◽  
Apparent Association

Oligosaccharides with four different types of branching were prepared from purified human transferrin, alpha 2-macroglobulin, caeruloplasmin and alpha 1-acid glycoprotein and labelled with NaBH3 3H. Binding of these oligosaccharides to rat liver plasma membrane, rat leucocytes, pig liver plasma membranes and pig leucocyte plasma membranes was investigated. A striking dependence of binding on oligosaccharide branching was observed. The values of apparent association constants Ka at 4 degrees C vary from 10(6) M-1 (biantennary structure) to 10(9) M-1 (tetra-antennary structure) in the liver, whereas in the leucocytes the Ka values were found to be of reversed order, from 1.8 × 10(9) M-1 for biantennary to 2.2 × 10(6) M-1 for tetra-antennary structures. The binding is completely inhibited by 150 mM-D-galactose, but 150 mM-D-mannose has almost no effect on binding. Leucocyte plasma membranes bind preferentially 125I-asialoglycoproteins with biantennary oligosaccharides, thus completing the specificity pattern of the hepatic recognition system for desialylated glycoproteins. Possible physiological roles of these two complementary recognition systems under normal and pathological conditions are discussed.

CNN-based multimodal touchless biometric recognition system using Gait and speech

2021 ◽  
pp. 1-10
Author(s):  
Sumit Sarin ◽  
Antriksh Mittal ◽  
Anirudh Chugh ◽  
Smriti Srivastava
Keyword(s):  
Neural Networks ◽  
Weighted Average ◽  
Recognition System ◽  
Biometric Recognition ◽  
Person Identification ◽  
Learning Techniques ◽  
Biometric Systems ◽  
Multimodal Biometric Systems ◽  
Biometric Features ◽  
Identification Model

Person identification using biometric features is an effective method for recognizing and authenticating the identity of a person. Multimodal biometric systems combine different biometric modalities in order to make better predictions as well as for achieving increased robustness. This paper proposes a touchless multimodal person identification model using deep learning techniques by combining the gait and speech modalities. Separate pipelines for both the modalities were developed using Convolutional Neural Networks. The paper also explores various fusion strategies for combining the two pipelines and shows how various metrics get affected with different fusion strategies. Results show that weighted average and product fusion rules work best for the data used in the experiments.

scholarly journals A Systemic Study of Pattern Recognition System Using Feedback Neural Networks

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Real Life ◽  
Recognition System ◽  
Person Identification ◽  
Recognition Error ◽  
Pattern Recognition System ◽  
Feedback Neural Network ◽  
Feedback Neural Networks

The evolution of artificial intelligence has led to the developments of various smart applications such as the pattern recognition models. Pattern recognition techniques has as widely applied in many real life applications such character recognition, speech recognition, and bio-metric authentication as well person identification. In this paper, we report on the detailed design of pattern recognition system using Hopfield Feedback Neural Network (HFNN) with the least possible recognition error. As a case study, we have applied the proposed HFNN model to recognize the decimal digits 0 - 9 where each image digit comprises a 12x10 pixels. The developed HFNN model has been efficiently used in recognizing the patterns with 20% random bit noise at maximum recognition accuracy. However, to assure the the least possible recognition error, we have trained our HFNN through the digit patterns’ perdition phase of 0% noisy patterns and the system was able to correctly predict all the patterns without any bit error. Finally, we have plotted all output patterns including the desired patterns, the training patterns, the 20% noisy patterns and recognized patterns, for comparison purposes and to gain more insights about the accuracy achieved by applying the proposed HFNN.

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.

scholarly journals Attacks on Biometric Systems: An Overview

2015 ◽  
Vol 1 (7) ◽  
pp. 283 ◽  
Author(s):  
Rubal Jain ◽  
Chander Kant
Keyword(s):  
Pattern Recognition ◽  
System Architecture ◽  
Recognition System ◽  
Biometric System ◽  
Knowledge Based ◽  
Biometric Template ◽  
Biometric Systems ◽  
Template Protection ◽  
Authentication System ◽  
Biometric Template Protection

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.

Partial Discharge Pattern Recognition of Power Transformer by Using Information Fusion

2011 ◽  
Vol 128-129 ◽  
pp. 933-937
Author(s):  
Xin Gang Chen ◽  
Yang Yang Zhao ◽  
Chao Feng Zhang ◽  
Xiao Xiao Tian
Keyword(s):  
Pattern Recognition ◽  
Information Fusion ◽  
Power Transformer ◽  
Partial Discharge ◽  
Recognition System ◽  
Experimental Models ◽  
Discharge Pattern ◽  
Different Types ◽  
Important Sense

As one of the most important equipments in the power system, partial discharge (PD) affects the transformer’s properties in a long-term period and the partial discharge pattern recognition has most important sense. In the paper,3 kinds of experimental models simulating discharges were designed and model experiments were performed. Based on this, a transformer partial discharge pattern recognition system based on information fusion technology is developed. the finally experiments show that: information fusion have enough ability to recognize different types of partial discharge.

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