Combining Human Ear and Profile Face Biometrics for Identity Recognition

Increasing environment noise pollution is a matter of great concern and of late has been attracting public attention. Sound produces the minute oscillatory changes in air pressure and is audible to the human ear when in the frequency range of 20Hz to 20 kHz. The chief sources of audible sound are the magnetic circuit of transformer which produces sound due to magnetostriction phenomenon, vibration of windings, tank and other structural parts, and the noise produced by cooling equipments. This paper presents the validation for sound level measurement scale, why A-weighted scale is accepted for sound level measurement, experimental study carried out on 10MVA Power Transformer. Also presents the outcomes of comparison between No-Load sound & Load sound level measurement, experimental study carried out on different transformer like - 10MVA, 50MVA, 100MVA Power Transformer, to define the dominant factor of transformer sound generation.

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The Asymmetrical Response of the Human Ear in Relation to the Problem of Combination Tones

Bulletin of the American Musicological Society ◽

10.1525/jams.1941.5.1.03a00130 ◽

1941 ◽

Vol 5 (1) ◽

pp. 19-20

Author(s):

Otto Stuhlman, Jr.

Keyword(s):

Human Ear

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Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.4.040404 ◽

2020 ◽

Vol 64 (4) ◽

pp. 40404-1-40404-16

Author(s):

I.-J. Ding ◽

C.-M. Ruan

Keyword(s):

Face Detection ◽

Speaker Recognition ◽

Visual Information ◽

Classification Tree ◽

Gaussian Mixture ◽

Recognition Method ◽

Indoor Space ◽

Identity Recognition ◽

Visual Identity ◽

Speaker Classification

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

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Human Ear Pattern Recognition System

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.35 ◽

2017 ◽

Vol 7 (8) ◽

pp. 117 ◽

Cited By ~ 1

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.

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