An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
An Evaluation of Face and Ear Biometrics
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Robust Real-Time Face Detection
International Journal of Computer Vision
Empirical Evaluation of Advanced Ear Biometrics
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Ear Recognition Based on Statistical Shape Model
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Ear Recognition by means of a Rotation Invariant Descriptor
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Ear Recognition using Improved Non-Negative Matrix Factorization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Localization of Ear Using Outer Helix Curve of the Ear
ICCTA '07 Proceedings of the International Conference on Computing: Theory and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Recognition Using 3D Ear Shape
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-matcher for ear authentication
Pattern Recognition Letters
Unified 3D face and ear recognition using wavelets on geometry images
Pattern Recognition
Force field feature extraction for ear biometrics
Computer Vision and Image Understanding
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
On shape-mediated enrolment in ear biometrics
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Advances in automatic gait recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Human ear recognition from face profile images
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Automated human identification using ear imaging
Pattern Recognition
Ear recognition based on local information fusion
Pattern Recognition Letters
Reliable ear identification using 2-D quadrature filters
Pattern Recognition Letters
Robust ear based authentication using Local Principal Independent Components
Expert Systems with Applications: An International Journal
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Ear recognition, as a biometric, has several advantages. In particular, ears can be measured remotely and are also relatively static in size and structure for each individual. Unfortunately, at present, good recognition rates require controlled conditions. For commercial use, these systems need to be much more robust. In particular, ears have to be recognized from different angles (poses), under different lighting conditions, and with different cameras. It must also be possible to distinguish ears from background clutter and identify them when partly occluded by hair, hats, or other objects. The purpose of this paper is to suggest how progress toward such robustness might be achieved through a technique that improves ear registration. The approach focuses on 2-D images, treating the ear as a planar surface that is registered to a gallery using a homography transform calculated from scale-invariant feature-transform feature matches. The feature matches reduce the gallery size and enable a precise ranking using a simple 2-D distance algorithm. Analysis on a range of data sets demonstrates the technique to be robust to background clutter, viewing angles up to ±13°, and up to 18% occlusion. In addition, recognition remains accurate with masked ear images as small as 20 × 35 pixels.