Visualization and Intelligent Systems Laboratory



Contact Information

Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425

Tel: (951)-827-3954

Bourns College of Engineering
NSF IGERT on Video Bioinformatics

UCR Collaborators:

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

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Alex Shin

Last updated: July 1, 2017




3D Ear Biometrics

The human ear is a new class of relatively stable biometrics. After decades of research of anthropometric measurements of ear photographs of thousands of people, it has been found that no two ears are alike, even in the cases of identical and fraternal twins, triplets, and quadruplets. It is also found that the structure of the ear does not change radically over time. Ear biometric has played a significant role in forensic science and its use by law enforcement agencies for many years but most of this work has been on analyzing the ear prints manually. Recent work on ear biometrics focuses on developing automated techniques for ear recognition. Ear biometrics can be based on a 2D gray scale or color image, 3D range image, or a combination of 2D and 3D images. Typically, an ear biometric system consists of ear detection and ear recognition modules.

Efficient Recognition of Highly Similar 3D Objects in Range Images

For rapid indexing and recognition of highly similar objects, we propose a novel method which combines feature embedding for the fast retrieval of surface descriptors, novel similarity measures for correspondence, and a support vector machine-based learning technique for ranking the hypotheses. By searching the nearest neighbors in low dimensions, the similarity between a model-test pair is computed using the novel features. The similarities for all model-test pairs are ranked using the learning algorithm to generate a short-list of candidate models for verification. The verification is performed by aligning a model with the test object. The experimental results, on the University of Notre Dame data set (302 subjects with 604 images) and the University of California at Riverside data set (155 subjects with 902 images) which contain 3D human ears, are presented and compared with the geometric hashing technique to demonstrate the efficiency and effectiveness of the proposed approach.

3D Free-form Object Recognition in Range Images using Local Surface Patches

Here we introduce an integrated local surface descriptor for surface representation and 3D object recognition. A local surface descriptor is characterized by its centroid, its local surface type and a 2D histogram. The 2D histogram shows the frequency of occurrence of shape index values vs. the angles between the normal of reference feature point and that of its neighbors. Instead of calculating local surface descriptors for all the 3D surface points, they are calculated only for feature points that are in areas with large shape variation. Experimental results with real range data are presented to demonstrate and compare the effectiveness and efficiency of the proposed approach with the spin image and the spherical spin image representations.

Human Ear Recognition in 3D

Range image and color image captured by a Minolta Vivid 300camera. The human ear is a new class of relatively stable biometrics that has drawn researchers’ attention. We proposed a human recognition system using 3D ear biometrics. For ear detection, we proposed a new approach which uses a single reference 3D ear shape model and locates the ear helix and the antihelix. For ear identification and verification, two new representations are proposed. These include the ear helix/antihelix representation obtained from the detection algorithm and the local surface patch (LSP) representation.

Human Ear Detection from Side Face Range Images

Ear detection is an important part of an ear recognition system. We have addressed human ear detection from side face range images. We introduce a simple and effective method to detect ears, which has two stages: offline model template building and on-line detection. The model template is represented by an averaged histogram of shape index. The on-line detection is a four-step process: step edge detection and thresholding, image dilation, connectcomponent labeling and template matching. Experiment results with real ear images are presented to demonstrate the effectiveness of our approach.

Global-to-Local Non-Rigid Shape Registration

Non-rigid shape registration is an important issue in computer vision. In this paper we propose a novel global-to- local procedure for aligning non-rigid shapes. The global similarity transformation is obtained based on the corresponding pairs found by matching shape context descriptors. The local deformation is performed within an optimization formulation, in which the bending energy of thin plate spline transformation is incorporated as a regularization term to keep the structure of the model shape preserved under the shape deformation. The optimization procedure drives the initial global registration towards the target shape that results in the one-to-one correspondence between the model and target shape. Experimental results demonstrate the effectiveness of the proposed approach.

Contour Matching for 3D Ear Recognition

Ear is a new class of relatively stable biometric that is invariant from childhood to early old age (8 to 70). It is not affected with facial expressions, cosmetics and eye glasses. We introduce a two-step ICP (Iterative Closest Point) algorithm for matching 3D ears. In the first step, the helix of the ear in 3D images is detected. The ICP algorithm is run to find the initial rigid transformation to align a model ear helix with the test ear helix. In the second step, the initial transformation is applied to selected locations of model ears and the ICP algorithm iteratively refines the transformation to bring model ears and test ear into best alignment. The root mean square (RMS) registration error is used as the matching error criterion. The model ear with the minimum RMS error is declared as the recognized ear. Experimental results on a dataset of 30 subjects with 3D ear images are presented to demonstrate the effectiveness of the approach.

Shape Model-Based 3D Ear Detection from Side Face Range Images

Ear detection is an important part of an ear recognition system. We propose a shape model-based technique for locating human ears in side face range images. The ear shape model is represented by a set of discrete 3D vertices corresponding to ear helix and anti-helix parts. Given side face range images, step edges are extracted considering the fact that there are strong step edges around the ear helix part. Then the edge segments are dilated, thinned and grouped into different clusters which are potential regions containing ears. For each cluster, we register the ear shape model with the edges. The region with the minimum mean registration error is declared as the detected ear region; the ear helix and anti-helix parts are meanwhile identified. Experiments are performed with a large number of real face range images to demonstrate the effectiveness of our approach. The contributions of this research are: (a) a ear shape model for locating 3D ears in side face range images, (b) an effective approach to detect human ears from side face range images, (c) experimental results on a large number of ear images.