AbstractIdentifying people has now become increasingly important due to rise in security concerns in public places. Ear biometrics gives us an opportunity to address this issue as ears are mostly visible unless occluded partly or completely. Ears are very stable, degrade little with age, and do not change with expressions as the face does. Their position on the side of the head allow easier detection by offering a predictable background as well as combination with other biometric cues. It. Also, it can be easily captured from a distance without letting the subject know, and hence data can be collected easily. This study describes the highly efficient ear detection and recognition systems that have been developed over the years.

Feature Extraction Techniques for Ear Biometrics : A SurveyAuthor : Shashank Dhariwal Supervisor : Dr. Sasan Mahmoodi (sd8g11|sm3@ecs.soton.ac.uk)

Force Field TransformForce Field Transform converts the image into a force field by performing invariant linear transform.

Each pixel is assumed to exert an isotropic force on other pixels proportional to the pixel’s intensity.

Force fields are used to determine parameters which describe angles and distances between detected energy maxima.

The force fields are then mapped onto a potential energy surface with a few potential energy wells and ridges which are used in matching stage.

Compression NetworksNeural networks are used to process face as general images for detection and analysis of facial features like eye distance and chin’s angle to approximate the position of the ear.

Use of compression network (CN) classifier comprises of two stages. • CN is trained auto-associatively on the

image to extract some properties. The vector generated constitutes the input to a perceptron which is responsible for identification task.

• The CN is trained as auto-associative memories which allow coding of the neural patterns in a small dimensional subspace by extracting important features.

Ear is modelled with an adjacency graph using curve segments.

The algorithm is as follows:• Image Acquisition done by taking

a 300 x 500 image of the subject’s head.

• Localization performed by using deformable contours on a Gaussian pyramid representation of image gradient.

• Computation of edges is done with the help of Canny operator and thresholding with hysteresis.

• Edge relaxation is done to form large curve segments and remove smaller ones.

• A Voronoi diagram of ear curves is made and a neighbourhood is built.

Voronoi Diagrams

Fig. 4: Neighbourhood Graph built using Voronoi diagram of ear curves [1]

Fig. 2: Building a compression network [2].

Fig. 5. (a) - Magnitude of Force Field after application on ear image. (b) - Manual initialization of 50 points, formation of energy lines, obtained energy maxima [3].

Iterative Closest PointIterative Closest Point (ICP) is employed for 3D shape recognition matching and use range images as the input data.

2D and 3D data adds to automatic extraction of ear where occlusion is separated and curve estimation is used to identify the ear pit.

It is followed by initialization of active contour based segmentation to obtain ear outlines.

A combination of 2D colour and 3D depth improves the robustness of the algorithm.

LSP & ICPEar detection starts with the extraction of regions-of-interest (ROIs) using both the range and colour images, followed by alignment of the reference ear shape model with ROIs. The local deformation driven by the optimization formulation drives the shape model more close to the ear helix and the antihelix parts.

Local Surface Patch (LSP) representation is done by using a set of descriptors computed at selected feature points followed by a two-step ICP algorithm for matching.

References[1] M. Burge and W. Burger, Ear Biometrics - In A. Jain, R. Bolle and S. Pankanti(Eds.), Biometrics: Personal Identification in a Networked Society, Kluwer Academic, (1998).[2] B. Moreno, A. Sanchez, J.F. Velez, On the Use of Outer Ear Images for Personal Identification in Security Applications, Proc. of IEEE Conf. On Security Technology, pp. 469-476, 1999.[3] D.J. Hurley , M.S. Nixon, J.N. Carter, Force Field Energy Functionals for Image Feature Extraction,, Image and Vision Computing Journal, vol. 20, no. 5-6, pp. 311-318, 2002.[4] K. Chang, K. Bowyer, S. Sarkar and B. Victor, Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, No 9, (2003)

[5] P. Yan, K. W. Bowyer, ICP-based approaches for 3D ear recognition, Proc. of SPIE Biometric Technology for Human Identification, 282291, 2005.[6] H. Chen, B. Bhanu, Human Ear Recognition in 3D, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 29, no. 4, 718-737, 2007.[7] A.H.M. Akkermans, T.A.M. Kevenaar and D.W.E. Schobben, Acoustic Ear Recognition for Person Identification, Proc Auto ID, Philips Research, 2005.

The ear by virtue of its special shape behaves like a filter so that a sound signal played into the ear is returned in a modified form.

An ear signature is generated by probing the ear with a sound signal which is reflected and picked up by a small device. The shape of the pinna and the ear canal determine the acoustic transfer function which forms the basis of the acoustic ear signature.

Linear Discriminant Analysis (LDA) can be applied to select the most discriminating components amongst the subjects.

Acoustic approach

Fig. 8. An acoustic probe wave is sent into the ear canal while a microphone receives the response [7].

PCAPrincipal Component Analysis (PCA) helps in identifying patterns and determining similarities as well as differences in data.

The algorithm is as follows:• Landmark points are recognized

and are used for cropping the image.

• Cropped image is normalized, masked to undergo histogram equalization, and obtain Eigen faces and Eigen Ears as a result.

Fig. 3: Points used for geometric normalization of face and ear images in the PCA-based approach [4].

Fig. 7. Top part of figure shows the ear detection module and bottom shows the ear recognition module using the ear helix/ antihelix and LSP representations [6].

Fig. 6. Starting from 2D/3D raw data: skin detection, curvature estimation, surface segmentation, region classification, ear pit detection [5].

Conclusion• Voronoi Diagrams succeed in avoiding the problem of localizing anatomical points and weakness of basing all feature

measurements on a single point but face the problem of occlusion due to hair and ear rings. • Compression Networks when combined with facial feature classifiers do not increase the identification rate as the

classifiers are independent. • Poor invariance remains a major issue for PCA. Another study shows that there is no significant difference between

ear and face recognition, and that the difference in performance of the two studies I due to less control over lighting conditions and occlusion.

• Force Field Transform offers robustness, reliability, invariance and excellent noise tolerance. However, it faces a problem when dealing with computation of higher- dimensional data.

• 3D Shape recognition shows that three dimensions can handle the problems of occlusion in a better way and give excellent results.

• Acoustic recognition demonstrates a completely different technique as it takes advantage of the ear’s acoustic features. It has an added advantage that it is low cost and publically acceptable.

The techniques developed have been very efficient and have matured to a certain level; the only glitch being their evaluation done under controlled environment. Issues such as occlusion, symmetry and individuality of ear as well as validity of ear prints need to be taken care of. With ear biometrics still evolving, we expect its greater utilization in the upcoming uni-modal and multi-modal recognition systems.

Ear Structure

Fig. 1. Iannarelli’s System (a) 1-Helix Rim, 2-Lobule, 3-Antihelix, 4-Concha, 5-Tragus, 6-Antitragus, 7-Crus of Helix, 8-Triangular Fossa, 9-Incisure Intertragica. (b) The locations of the anthropometric measurements used in the Iannarelli System [1].

Iannarelli developed an ear recognition system based upon 12 measurements between several landmark points . He made two large-scale human ear identification studies consisting of a set of 10000 ears, and the other a set of ears of identical twins and triplets.

He concluded that ears in both the sets were unique and that the twins and triplets had similar but not identical ear structures.