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 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.

Implementation of Embedded Unspecific Continuous English Speech Recognition Based on HMM

2021 ◽  
Vol 14 ◽  
Author(s):  
Xiaoli Lu ◽  
Mohd Asif Shah
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Vital Role ◽  
Recognition Algorithm ◽  
Theoretical Research ◽  
Fast Speed ◽  
Conversational Interfaces ◽  
Recognition Function ◽  
High Recognition Accuracy

Background: Human-computer interaction plays a vital role through Natural Language Conversational Interfaces to improve the usage of computers. Speech recognition technology allows the machine to understand human language. A speech recognition algorithm is used to achieve this function. Methodology: This paper is mainly based on the fundamental theoretical research of speech signals, establishes the HMM model, uses speech collection, recognition, and other methods, simulates on MATLAB, and integrates the recognition system ported to ARM for debugging and running to realize the embedded speech recognition function based on HMM under the ARM platform. Conclusion: The conclusion shows that the HMM-based embedded unspecific continuous English speech recognition system has high recognition accuracy and fast speed.

scholarly journals A Recognition System for Subjects' Signature Using the Spatial Distribution of Signature Body

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Zainab J. Ahmed ◽  
Loay E. George
Keyword(s):  
Feature Extraction ◽  
Standard Deviation ◽  
Feature Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Data File ◽  
Distance Measures ◽  
Identification System ◽  
Rotation Compensation

This investigation proposed an identification system of offline signature by utilizing rotation compensation depending on the features that were saved in the database. The proposed system contains five principle stages, they are: (1) data acquisition, (2) signature data file loading, (3) signature preprocessing, (4) feature extraction, and (5) feature matching. The feature extraction includes determination of the center point coordinates, and the angle for rotation compensation (θ), implementation of rotation compensation, determination of discriminating features and statistical condition. During this work seven essential collections of features are utilized to acquire the characteristics: (i) density (D), (ii) average (A), (iii) standard deviation (S) and integrated between them (iv) density and average (DA), (v) density and standard deviation (DS), (vi) average and standard deviation (AS), and finally (vii) density with average and standard deviation (DAS). The determined values of features are assembled in a feature vector used to distinguish signatures belonging to different persons. The utilized two Euclidean distance measures for matching stage are: (i) normalized mean absolute distance (nMAD) (ii) normalized mean squared distance (nMSD). The suggested system is tested by a public dataset collect from 612 images of handwritten signatures. The best recognition rate (i.e., 98.9%) is achieved in the proposed system using number of blocks (21×21) in density feature set. With the same number of blocks (i.e., 21×21) the maximum verification accuracy obtained is (100%).

scholarly journals On the Determination of the Chip Nozzle Recognition System by Using Machine Vision

2021 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Jing Qiu ◽  
Yun Xu ◽  
Siyi Liu
Keyword(s):  
Machine Vision ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Analysis Method ◽  
Practical Application ◽  
Average Value ◽  
Blob Analysis ◽  
Sample Data ◽  
Semiconductor Chips

To solve the problem of chip damage caused by the using the wrong type of vacuum nozzle during the packaging of semiconductor chips. A recognition system of vacuum nozzle based on machine vision was proposed. In this research, 29 kinds of lifting nozzles are selected as test samples. The backlight intensity of two lifting nozzle images (one strong and one weak separately) is collected at the first beginning. Then, the Blob analysis method is using to analyze the weak backlighting image. The area of the lifting nozzle and the minimum outer rectangular feature can be obtained subsequently. To identify the shape of the liftin nozzle (round or square), the area ratio is calculated. At the same time, the minimum outer rectangular of the lifting nozzle is selected as the reference rectangle. Then, construct the measurement rectangle. The 2-dimensional size of the lifting nozzle is measured as well. Meanwhile, for the strong backlight image, the average value of the grayscale which located within the minimum outer rectangle is calculated. Therefore, the color (black, white, or beige) of the nozzle can be identified. Finally, the sample data is saved to the database as the sample database. During the recognition process, the shape, color, and size of the lifting nozzle being analyzing are using as the parameter to realize the condition inquire. The experimental results show that the recognition accuracy of this method is 98.85%, and the recognition time of one nozzle is around 1 second, which meets the requirements of practical application.

Keyword Recognition Based on MFCC

2014 ◽  
Vol 926-930 ◽  
pp. 1729-1732
Author(s):  
Sha Yang ◽  
Tian Hu ◽  
Yun Lu Zhang
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Hidden Markov ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Speech Recognition System ◽  
Continuous Speech Recognition ◽  
Recognition Time ◽  
State 1 ◽  
Model Approach

After about 50 years of development, speech recognition technology has been able to achieve large vocabulary, non-specific human continuous speech recognition system. On account of Chinese pronunciation features, we research the small vocabulary, non-specific Chinese speech recognition based on continuous Hidden Markov Model approach. With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.

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%.

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