AR Model Based Human Identification using Ear Biometrics

Khursheed, Farida; Mir, Ajaz Hussain

doi:10.14257/ijsip.2014.7.3.28

Cited by 9 publications

(6 citation statements)

References 41 publications

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“…For each of the component, contours are computed, where each contour being a sequence of consecutive boundary points. It is given as: (6) where Mi is the length of contour i. Then calculation of the Freeman chain code can be done that is associated with each contour.…”

Section: B Methodology Based On Chain Codementioning

confidence: 99%

“…The system trained and tested on two different data sets of 650 and 225 writers respectively. Farida Khursheed, et.al [6] proposed a method on usefulness of time series based Auto Regressive (AR) modelling technique has been explored for identification of a person. For this purpose, time series is obtained from the contour coordinates of the ear.…”

Section: Literature Surveymentioning

confidence: 99%

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Image Retrieval using Chain code and Autoregression

Shweta¹,

Sachin²

2017

IJCA

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Over the years passed has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Similarly, digital image retrieval has expanded in many directions that are resulting into explosion in the volume of image data required to be organized. This paper presents a framework for image retrieval based on chain code and auto regression that helps to achieve higher retrieval efficiency. In this paper, we discuss about the key contributions of the methodology that is followed while performing experiment for image retrieval based on chain code and auto regression. Here comparative study of results and also efficiency of both these image retrieval techniques are discussed which are obtained while experimentation.

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Section: B Methodology Based On Chain Codementioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Image Retrieval using Chain code and Autoregression

Shweta¹,

Sachin²

2017

IJCA

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“…The method of extraction edge of ear structural features based on 2D grayscale image recognition is commonly used: Canny operator [12][13][14] feature edge extraction; Moreno [15] using Sobel operator feature edge detection; Choras [16] using the K-value method to extract the edge of the outer ear characteristics curve, not only extract the edge width, but also need to manually set the threshold K, cannot meet the need of automatic processing; Mu Zhichun [17] use different methods to extract of the inner ear structural feature sedge edge and the outer ear shape features, and then combine them; literature [18] using the high-frequency component of the wavelet decomposition to extract edge information. These methods are generally small amount of calculation, intuitive sense of physical characteristics.…”

Section: Figure 1 Anatomy Of Earmentioning

confidence: 99%

Ear Structure Feature Extraction Based on Multi-scale Hessian Matrix

Ma¹,

Ban²,

Wang³

et al. 2013

IJSIP

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In this paper, a new ear anatomy feature edge extraction method based on Hessian matrix is proposed. Stable edge is obtained from principal curvature image across scale space. Firstly, the side face image that includes an ear is filtered and forms Gaussian pyramid. Secondly, the 2D gray image in the pyramid was regarded as a surface, maximum and minimum principal curvature and their direction were calculated by using Hessian matrix, and principal curvature image was formed. The characteristic of surface is that gray level changes in edge area is sharp and the curvature is larger compared to that of the smooth area. In accordance with this characteristic, automatic hysteresis thresholding based on curvature direction flow is used to segment curvature images. Lastly, combine different scale threshold images to get the feature edge image. The experiments demonstrate that extracted feature edge is smooth and connected. New method is robust to noise, and is sensitive to the weak edge, using Hausdorff distance as similarity measurement of two edge images can obtain above 96% recognition rate.

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“…The rising number of conflicts between cyber security dangers and ordinary security techniques for workforce identification has led to biometric security systems. As it is not possible for some particular human attributes to be stolen or altered, utilisation of biometrics has been a subject of incredible significance for individual identification (Khursheed and Mir, 2014).…”

Section: Introductionmentioning

confidence: 99%

Feature level fusion using physical biometric traits

Carol

Fred²

2018

IJBET

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Biometric is a measuring technique that is utilised to discern a person's identity through a physiological or behavioural feature. Each modality on its own cannot always be reliable for recognition. The multimodal biometric system offers several advantages over the traditional biometric system. In order to overcome the limitations of the unimodal biometrics, the fusion of multimodal biometric traits have been used. In this paper, a multimodal biometric based on fingerprint, palm print and knuckle print is presented. Gabor filter is employed to extract features from these biometric traits. The features extracted from these biometric traits are normalised and fused together. The features are fused by feature level fusion, and the result of the proposed method provides efficient authentication, by minimising the FAR by 1.43%, and the framework achieves satisfactory performance.

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AR Model Based Human Identification using Ear Biometrics

Abstract: In this paper usefulness of time series based Auto Regressive (AR) modelling

Cited by 9 publications

References 41 publications

Image Retrieval using Chain code and Autoregression

Image Retrieval using Chain code and Autoregression

Ear Structure Feature Extraction Based on Multi-scale Hessian Matrix

Feature level fusion using physical biometric traits

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