On Shape-Mediated Enrolment in Ear Biometrics

Arbab-Zavar, Banafshe; Nixon, Mark S.

doi:10.1007/978-3-540-76856-2_54

Cited by 41 publications

(43 citation statements)

References 8 publications

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“…A tracking method, which combined both skin-color model and intensity contour information, was proposed by Yuan and Mu [14] to detect and track the ear in sequence frames. Another ear detection technique is based on finding the elliptical shape of the ear using a Hough Transform (HT) accruing tolerance to noise and occlusion [15]. In [16], Cummings et al utilized the image ray transform to detect ears.…”

Section: Ear Detectionmentioning

confidence: 99%

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

Zhang

2017

Symmetry

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Abstract:Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN) to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions), 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2) and University of Beira Interior Ear dataset (UBEAR), which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

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Section: Ear Detectionmentioning

confidence: 99%

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

Zhang

2017

Symmetry

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“…They are designed to highlight specific properties of the outer ear, which occur in each image where the ear is visible no matter in which pose the ear has been photographed. In [38] the Hough transform is used for enhancing regions with a high density of edges. In head profile images, a high density of edges especially occurs in the ear region (see Figure 6(a)).…”

Section: Ear Detectionmentioning

confidence: 99%

“…In head profile images, a high density of edges especially occurs in the ear region (see Figure 6(a)). In [38] it is reported that the Hough transform based ear detection gets trapped when people wear glasses since the frame introduces additional edges to the image. This especially occurs in the eye and nose region.…”

Section: Ear Detectionmentioning

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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“…The second technique applied the algorithm described by Arbab-Zavar [10] to register the ear automatically, using the outer ear ellipse. In both cases the intensity values had their mean and standard deviation normalised.…”

Section: Comparison Implementationsmentioning

confidence: 99%

“…Arbab-Zavar et al [10] have proposed an enrolment technique exploiting the elliptical shape of the outer ear. This has produced good results with occlusion, but the accuracy of registration is much less than can be achieved manually.…”

Section: Introductionmentioning

confidence: 99%

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Bustard

Nixon

2008

2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems

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Abstract-Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to ±13 degrees and with over 20% occlusion.

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On Shape-Mediated Enrolment in Ear Biometrics

Cited by 41 publications

References 8 publications

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

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

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

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