On the use of outer ear images for personal identification in security applications

Moreno, Belén; Sánchez, Araceli; Vélez, José Francisco

doi:10.1109/ccst.1999.797956

Cited by 130 publications

(74 citation statements)

References 7 publications

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“…Therefore we do not overrule that our methods are able to identify humans better than any other biometrics. However, our algorithms tested for real-life medium-security access control system for limited number of users are satisfactory and comparable to 2D ear image recognition results close to 100% reported by other groups [12,14,15].…”

Section: Discussionmentioning

confidence: 74%

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Perspective methods of human identification: Ear biometrics

Choraś

2008

Opto-Electronics Review

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Geometrical methods of feature extraction from ear images in order to perform human identification are presented. Geometrical approach is motivated by the actual procedures used by police and forensic experts (so-called ear otoscopy). In their work, geometrical features of ears such as size, height, width, and shapes of earlobe are useful and valid proofs of identity. The contribution of the article is development of the new and original methods of geometrical feature extraction from 2D ear images. Four novel algorithms of ear feature extraction from contour images are described in detail. Moreover, identification results obtained for each of the methods, based on the distance of feature vectors in the feature space, are presented.

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Section: Discussionmentioning

confidence: 74%

“…Their method, however, was not fully automated, since the reference points had to be manually inserted into images. Another approach to ear image feature extraction was presented by Moreno et al [15]. Their work was based on macrofeatures extracted by compression networks.…”

Section: Related Workmentioning

confidence: 99%

Perspective methods of human identification: Ear biometrics

Choraś

2008

Opto-Electronics Review

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“…For ear recognition, they have proposed graph matching algorithm which was suitable only for passive identification. Moreno also proposed a new method for ear recognition based on outer ear contour's feature points and information that can be obtained from shape and wrinkles present in the ear [8]. In [1], Hurley proposed an approach based on force field transform in which the ear image is considered as an array of Gaussian attractors that act as a source of force field.…”

Section: Introductionmentioning

confidence: 99%

Personal Identification Using Ear Recognition

Tariq

Akram

2012

TELKOMNIKA

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Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics Keywords: authentication, biometrics, ear recognition, Haar wavelet, personal identification IntroductionBiometrics deals with identification of individuals on the basis of their physiological and behavioral characteristics. Biometric technology is gaining popularity in this modern era for the purpose of security and other applications.The new feature in biometrics is human ear which is becoming popular. It has several advantages over other biometric technologies such as iris, fingerprints, face and retinal scans. Ear is large as compared to iris [1] and fingerprint and unlike them, the image acquisition of human ear is very easy as it can be captured from a distance without the cooperation of individual [2]. Human ear contains rich and stable features and it is more reliable than face as the structure of ear is not subject to change with the age and facial expressions [3]. The anatomy of human ear is given in Figure 1. It shows the standard features of human ear. It has been found that no two ears are exactly the same even that of identical twins [4], [5]. Therefore it appears that ear biometrics is a good solution for computerized human identification and verification systems. The major application of this technology is crime investigation. Ear features have been used for many years in the forensic sciences for recognition.There are many approaches in the literature for an automated ear recognition system. Alfred Iannarelli [6] was a pioneer in this field. He classified the ear into eight parts on the basis of 12 measurements. But this method was not suitable because of the difficulty of localization of anatomical points [6].

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“…Yuizono et al [22] treated the problem as an optimization task, and proposed a specially-developed genetic local search. Moreno et al [16] used different combinations of several neural classifiers. Principal Components Analysis (PCA) approaches have also been applied in a number of studies [3], [20], [13].…”

Section: Introductionmentioning

confidence: 99%

On Model-Based Analysis of Ear Biometrics

Arbab-Zavar

Nixon

Hurley

2007

2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems

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Abstract-Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Most current approaches are holistic and describe the ear by its general properties. We propose a new model-based approach, capitalizing on explicit structure and with the advantages of being robust in noise and occlusion. Our model is a constellation of generalized ear parts, which is learned off-line using an unsupervised learning algorithm over an enrolled training set of 63 ear images. The Scale Invariant Feature Transform (SIFT), is used to detect the features within the ear images. In recognition, given a profile image of the human head, the ear is enrolled and recognised from the parts selected via the model. We achieve an encouraging recognition rate, on an image database selected from the XM2VTS database. A head-to-head comparison with PCA is also presented to show the advantage derived by the use of the model in successful occlusion handling.

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On the use of outer ear images for personal identification in security applications

Cited by 130 publications

References 7 publications

Perspective methods of human identification: Ear biometrics

Perspective methods of human identification: Ear biometrics

Personal Identification Using Ear Recognition

On Model-Based Analysis of Ear Biometrics

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