Exploratory study on classification and individualisation of earprints

Meijerman, Lynn; Sholl, Sarah; Conti, Francesca; Giacon, Marta; Lugt, Cor van der; Drusini, Andrea G.; Vanezis, Peter; Maat, G. J. R.

doi:10.1016/j.forsciint.2003.10.024

Cited by 57 publications

(35 citation statements)

References 5 publications

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“…Even though Iannarelli's work lacks a complex theoretical basis, it is commonly believed that the shape of the outer ear is unique. The studies in [5] and [6] show that all ears of the investigated databases posses individual characteristics, which can be used for distinguishing between them. Because of the lack of a sufficiently large ear database, these studies can only be seen as hints,not evidence, for the outer ear's uniqueness.…”

Section: Figure 1: Characteristics Of the Human Ear The German Criminmentioning

confidence: 99%

Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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The possibility of identifying people by the shape of their outer ear was first discovered by the French criminologist Bertillon, and refined by the American police officer Iannarelli, who proposed a first ear recognition system based on only seven features.The detailed structure of the ear is not only unique, but also permanent, as the appearance of the ear does not change over the course of a human life. Additionally, the acquisition of ear images does not necessarily require a person's cooperation but is nevertheless considered to be non-intrusive by most people.Because of these qualities, the interest in ear recognition systems has grown significantly in recent years. In this survey, we categorize and summarize approaches to ear detection and recognition in 2D and 3D images. Then, we provide an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which we consider to be the most important application of ear recognition characteristic in the near future.

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Section: Figure 1: Characteristics Of the Human Ear The German Criminmentioning

confidence: 99%

Ear biometrics: a survey of detection, feature extraction and recognition methods

2012

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“…Empirical evidence supporting the ear's uniqueness was later provided by Iannarelli, who examined over 10,000 ear samples and developed a manual system for ear identification [41]. Ear prints have also been used as forensic evidence [55,24]. Despite the use of ear evidence in forensics for many years, machine vision approaches to ear identification are relatively new.…”

Section: Contextmentioning

confidence: 99%

“…Empirical evidence supporting the ear's uniqueness was later provided in two studies by Iannarelli, examining over 10,000 ears [41]. Earprints have also been considered as a possible source of forensic evidence [55,24].…”

Section: Chapter 2 Ear Biometricsmentioning

confidence: 99%

On guided model-based analysis for ear biometrics

Arbab-Zavar

Nixon

2011

Computer Vision and Image Understanding

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Ears are a new biometric with major advantage in that they appear to maintain their structure with increasing age. Current approaches have exploited 2D and 3D images of the ear in human identification. Contending that the ear is mainly a planar shape we use 2D images, which are consistent with deployment in surveillance and other planarimage scenarios. So far ear biometric approaches have mostly used general properties and overall appearance of ear images in recognition, while the structure of the ear has not been discussed. In this thesis, we propose a new model-based approach to ear biometrics. Our model is a part-wise description of the ear structure. By embryological evidence of ear development, we shall show that the ear is indeed a composite structure of individual components. Our model parts are derived by a stochastic clustering method on a set of scale invariant features on a training set. We shall review different accounts of ear formation and consider some research into congenital ear anomalies which discuss apportioning various components to the ear's complex structure. We demonstrate that our model description is in accordance with these accounts. We extend our model description, by proposing a new wavelet-based analysis with a specific aim of capturing information in the ear's outer structures. We shall show that this section of the ear is not sufficiently explored by the model, while given that it exhibits large variations in shape, intuitively, it is significant to the recognition process. In this new analysis, log-Gabor filters exploit the frequency content of the ear's outer structures.In recognition, ears are automatically enrolled via our new enrolment algorithm, which is based on the elliptical shape of ears in head profile images. These samples are then recognized via the parts selected by the model. The incorporation of the wavelet-based analysis of the outer ear structures forms an extended or hybrid method. The performance is evaluated on test sets selected from the XM2VTS database. By results, both ii in modelling and recognition, our new model-based approach does indeed appear to be a promising new approach to ear biometrics. In this, the recognition performance has improved notably by the incorporation of our new wavelet-based analysis.The main obstacle hindering the deployment of ear biometrics is the potential occlusion by hair. A model-based approach has a further attraction, since it has an advantage in handling noise and occlusion. Also, by localization, a wavelet can offer performance advantages when handling occluded data. A robust matching technique is also added to restrict the influence of corrupted wavelet projections. Furthermore, our automatic enrolment is tolerant of occlusion in ear samples. We shall present a thorough evaluation of performance in occlusion, using PCA and a robust PCA for comparison purposes. Our hybrid method obtains promising results recognizing occluded ears. Our results have confirmed the validity of this approach both in modelling and recognition. ...

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“…It has been used as tamper-proof complementary evidence to the fingerprint and DNA evidence because the courts of law required two or more types of evidence to place a suspect at a scene of crime (Nanni & Lumini 2009) [7]. The excellent evidential value of ear prints is identified with the higher level of inter-person variation and lower level of intra-person variation (Meijerman., et al 2006a) [6].…”

Section: Introductionmentioning

confidence: 96%

“…Although this might affect the chance of finding a print in a database after a long period of time, it is unlikely to make the ears less unique. In addition, the natural changes in the ears are small enough and suitable to be used in forensic practice and legal prosecution (Meijerman., et al 2006a) [6]. The unique ear print has been used in forensic medicine and criminal investigations as an identification proof, for example, in airplane crashes or crime scenes.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study on Earprint Identification among Young Adults in Malaysia

Lai¹,

Osman²,

Ismail³

2017

Journal of Forensic Science & Criminology

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The objective of this study was to study ear biometric for identification purpose among young adults in Malaysia. A total of 148 subjects (74 males and 74 females) ranging in age from 18 to 25 years were taken from Campus Kuala Lumpur of National University of Malaysia (UKMKKL). A total of twelve biometrics based on Iannarelli's method as well as ear length and ear width were used as measurements. Statistical analysis indicated that left and right ears had no significant difference (p > 0.05). Gender difference was found to be highly significant for nine out of fourteen ear biometrics (p < 0.05). However, there were only five out of fourteen ear biometrics had significant difference between Chinese and Malays (p < 0.05). Statistical analysis had also indicated that differences among ages for each ear biometrics were insignificant and no clear correlation between ear biometrics and ages was obtained (p > 0.05). Formulae for gender and race estimation were obtained by using Classification and Regression (C & R) Tree. Formulae of ear length had the highest accuracy (78.7%) for gender estimation.

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Exploratory study on classification and individualisation of earprints

Cited by 57 publications

References 5 publications

Ear biometrics: a survey of detection, feature extraction and recognition methods

Ear biometrics: a survey of detection, feature extraction and recognition methods

On guided model-based analysis for ear biometrics

A Preliminary Study on Earprint Identification among Young Adults in Malaysia

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