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

Pflug, Anika; Busch, Christoph

doi:10.1049/iet-bmt.2011.0003

Cited by 190 publications

(105 citation statements)

References 74 publications

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“…T ∈ R 2M , we define the selection matrices S ∈ [0, 1] 3M ×3N that select the M vertices which are the correspondences of the 2D landmarks. This linear system can be written as:…”

Section: Mesh Editing Regularised By 2d Landmarksmentioning

confidence: 99%

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A Data-Augmented 3D Morphable Model of the Ear

Dai

Pears

Smith

2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Abstract-Morphable models are useful shape priors for biometric recognition tasks. Here we present an iterative process of refinement for a 3D Morphable Model (3DMM) of the human ear that employs data augmentation. The process employs the following stages 1) landmark-based 3DMM fitting; 2) 3D template deformation to overcome noisy over-fitting; 3) 3D mesh editing, to improve the fit to manual 2D landmarks. These processes are wrapped in an iterative procedure that is able to bootstrap a weak, approximate model into a significantly better model. Evaluations using several performance metrics verify the improvement of our model using the proposed algorithm. We use this new 3DMM model-booting algorithm to generate a refined 3D morphable model of the human ear, and we make this new model and our augmented training dataset public.

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“…T ∈ R 2M , we define the selection matrices S ∈ [0, 1] 3M ×3N that select the M vertices which are the correspondences of the 2D landmarks. This linear system can be written as:…”

Section: Mesh Editing Regularised By 2d Landmarksmentioning

confidence: 99%

“…The shape of the ear has long been recognised as a means of biometric identification [1], [2], [3], [4]. There are many existing ear recognition systems in the literature [5], [6], [7], [8], [9], [10], with a recent survey by Emersic et al [11] and ear biometrics continues to be an active research area [12].…”

Section: Introductionmentioning

confidence: 99%

A Data-Augmented 3D Morphable Model of the Ear

Dai

Pears

Smith

2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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“…However, these studies were limited to in-plane rotations [3][4][5]. The authors developed the technologies necessary to implement criminal investigation-assistance systems for ear images.…”

Section: Related Studiesmentioning

confidence: 99%

Another Attempt at Estimating the Camera Angle in Ear Recognition

Watabe

Minamidani

Sai

2018

IJAE

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It is known that criminal investigation assistance systems that use face images are unreliable when profile images are queried. Although frontal facial recognition has matured, profile facial recognition is not yet well developed. To compensate for such unreliability, the use of ear recognition is a promising direction, as ear shapes are known to be unique and visible in profile images. However, the ear has a complex, three-dimensional concave shape. Thus, accurate ear identification is challenging when the angle of an ear image from a surveillance camera is different from that of images in an image database. Some studies (including our past work) have addressed this issue and improved the robustness of ear recognition against off-angle ear rotation. However, there is room for improvement in this area. To improve on our earlier work on single view-based ear biometrics, another estimation method for the camera angle of an ear image based on principal component analysis is examined in this study. Experimental results show that the proposed method can improve the accuracy of the estimation of the camera angle. In conjunction with this enhancement, the improvement in robustness against off-angle ear rotation is examined. Therefore, the proposed method improves the robustness of ear recognition by accurately calculating the camera angle of the image of the ear.

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“…Xie and MU [32] proposed an improved locally linear embedding (LLE) algorithm for reducing the dimensionality of ear features. A general classification algorithm based on the theory of compressive sensing has been used in [33] A complete survey of all the approaches of ear biometrics is given in [34,35].…”

Section: Statistical Approachmentioning

confidence: 99%