Researchers have suggested that the ear may have advantages over the face for biometric recognition. Our previous experiments with ear and face recognition, using the standard principal component analysis approach, showed lower recognition performance using ear images. We report results of similar experiments on larger data sets that are more rigorously controlled for relative quality of face and ear images. We find that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent, respectively, in one experiment. We also find that multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric, for example, 90.9 percent in the analogous experiment.

/pdf/comparison-and-combination-of-ear-and-face-images-in-4p1qw10m60.pdf

Comparison and combination of ear and face images in appearance-based biometrics

The present invention provides a system and method for facilitating a transaction using a secondary transaction number that is associated with a cardholder's primary account. The cardholder provides the secondary transaction number, often with limited-use conditions associated therewith, to a merchant to facilitate a more secure and confident transaction.

System for facilitating a transaction

Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.

Biometric Recognition Using 3D Ear Shape

A review of biometric technology along with trends and prospects

Human ear is a new class of relatively stable biometrics that has drawn researchers' attention recently. In this paper, we propose a complete human recognition system using 3D ear biometrics. The system consists of 3D ear detection, 3D ear identification, and 3D ear verification. For ear detection, we propose a new approach which uses a single reference 3D ear shape model and locates the ear helix and the antihelix parts in registered 2D color and 3D range images. For ear identification and verification using range images, two new representations are proposed. These include the ear helix/antihelix representation obtained from the detection algorithm and the local surface patch (LSP) representation computed at feature points. A local surface descriptor is characterized by a centroid, a local surface type, and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values versus the angles between the normal of reference feature point and that of its neighbors. Both shape representations are used to estimate the initial rigid transformation between a gallery-probe pair. This transformation is applied to selected locations of ears in the gallery set and a modified iterative closest point (ICP) algorithm is used to iteratively refine the transformation to bring the gallery ear and probe ear into the best alignment in the sense of the least root mean square error. The experimental results on the UCR data set of 155 subjects with 902 images under pose variations and the University of Notre Dame data set of 302 subjects with time-lapse gallery-probe pairs are presented to compare and demonstrate the effectiveness of the proposed algorithms and the system

Human Ear Recognition in 3D

https://www.cse.msu.edu/~rossarun/BiometricsTextBook/Papers/OtherBiometrics/Hurley_ForceFieldEar_CVIU05.pdf

Force field feature extraction for ear biometrics

Force Field Energy Functionals for Image Feature Extraction

It is more than 10 years since the first tentative experiments in ear biometrics were conducted and it has now reached the “adolescence” of its development towards a mature biometric. Here we present a timely retrospective of the ensuing research since those early days. Whilst its detailed structure may not be as complex as the iris we show that the ear has unique security advantages over other biometrics. It is most unusual even unique in that it supports not only visual and forensic recognition but also acoustic recognition at the same time. This together with its deep three-dimensional structure and its robust resistance to change with age will make it very difficult to counterfeit thus ensuring that the ear will occupy a special place in situations requiring a high degree of protection.

/pdf/the-ear-as-a-biometric-1ftlyou0yj.pdf

The ear as a biometric

The objective in defining feature space is to reduce the dimension of pattern space yet maintaining discriminatory power for classification, together with invariant description. To meet this objective in the context of ear biometrics a novel force field transformation has been developed in which the image is treated as an array of Gaussian attractors that act as the source of a force field. The directional properties of the force field are exploited to automatically locate a small number of potential energy wells and channels that form the basis of the ear description. This has been applied to a small database of ears and initial results show that the new approach can be used for automatic ear recognition.

Automatic ear recognition by force field transformations

The objective in defining feature space is to reduce the dimension of the original pattern space yet maintaining discriminatory power for classification. To meet this objective in the context of ear and face biometrics a novel force field transformation has been developed in which the image is treated as an array of Gaussian attractors that act as the source of a force field. The directional properties of the force field are exploited to automatically locate a small number of potential energy wells and channels that form the basis of a characteristic feature vector. Here, we generalise the analysis, and the stock of applications.

/pdf/a-new-force-field-transform-for-ear-and-face-recognition-295ubkdsxl.pdf

David J. Hurley

Papers

Force field feature extraction for ear biometrics

Force Field Energy Functionals for Image Feature Extraction

The ear as a biometric

Automatic ear recognition by force field transformations

A new force field transform for ear and face recognition