On Model-Based Analysis of Ear Biometrics

Arbab-Zavar, Banafshe; Nixon, Mark S.; Hurley, David J.

doi:10.1109/btas.2007.4401937

Cited by 36 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In our other work we have introduced a new model-based ear recognition [11] in which the ear is recognised from the parts selected via a model. Combining our enrolment with the model we have built an automatic ear biometric system in which the objective of handling occlusion is carried through in enrolment via the HT and in feature extraction via the use of our new model which is contrary to holistic methods that describe images by their overall appearance.…”

Section: Discussionmentioning

confidence: 99%

On Shape-Mediated Enrolment in Ear Biometrics

Arbab-Zavar

Nixon

Advances in Visual Computing

Self Cite

View full text Add to dashboard Cite

Abstract. Ears are a new biometric with major advantage in that they appear to maintain their shape with increased age. Any automatic biometric system needs enrolment to extract the target area from the background. In ear biometrics the inputs are often human head profile images. Furthermore ear biometrics is concerned with the effects of partial occlusion mostly caused by hair and earrings. We propose an ear enrolment algorithm based on finding the elliptical shape of the ear using a Hough Transform (HT) accruing tolerance to noise and occlusion. Robustness is improved further by enforcing some prior knowledge. We assess our enrolment on two face profile datasets; as well as synthetic occlusion.

show abstract

Section: Discussionmentioning

confidence: 99%

On Shape-Mediated Enrolment in Ear Biometrics

Arbab-Zavar

Nixon

Advances in Visual Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…(a) Concentric Circles [57] (b) SIFT features [53] (c) Active Contour [62] (d) Force Field [41] Figure 7: Examples for feature extraction for 2D ear images.…”

Section: Holistic Descriptorsmentioning

confidence: 99%

“…Hence the landmarks have to be filtered before the actual comparison can start. Arbab-Zavar et al [53] as well as Badrinath and Gupta [54] therefore train a reference landmark model, which only contains a small number of non-redundant landmarks. This landmark model is used for filtering the SIFT landmarks, which were initially detected in the probe and reference ear.…”

Section: Local Descriptorsmentioning

confidence: 99%

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

2012

View full text Add to dashboard Cite

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.

show abstract

“…In this paper, a fusion approach of face [4] and ear [5] biometrics using Dempster-Shafer decision theory [7] is proposed. It is known that face biometric [4] is most widely used and is one of the challenging biometric traits, whereas ear biometric [5] is an emerging authentication technique and shows significant improvements in recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…It is known that face biometric [4] is most widely used and is one of the challenging biometric traits, whereas ear biometric [5] is an emerging authentication technique and shows significant improvements in recognition accuracy. Fusion of face and ear biometrics has not been studied in details except the work presented in [6].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Belief Fusion for Face and Ear Biometrics

Kisku¹,

Gupta

Mehrotra

et al. 2009

IIM

View full text Add to dashboard Cite

This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on two multimodal databases, namely, IIT Kanpur database and virtual database. Former contains face and ear images of 400 individuals while later consist of both images of 17 subjects taken from BANCA face database and TUM ear database. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.

show abstract

On Model-Based Analysis of Ear Biometrics

Cited by 36 publications

References 14 publications

On Shape-Mediated Enrolment in Ear Biometrics

On Shape-Mediated Enrolment in Ear Biometrics

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

Multimodal Belief Fusion for Face and Ear Biometrics

Contact Info

Product

Resources

About