3-dimensional Ear Recognition based iterative Closest Point with stochastic Clustering Matching

Ear recognition is a new technology and future tren d for personal identification. However, the false d etection rate and matching recognition are very challenging due to the ear complex geometry. The Scope of the study is to introduced a combination of Iterative Closest Po int (ICP) and Stochastic Clustering Matching (SCM) algorithm for 3D ears matching based on biometrics field with a good steadiness to reduce the false de tection rate. The corresponding ear extracts from the side range image and characterized by 3D features. The proposed method used matlab simulation and defined the average detection time 35ms and an identification simila rity is 98.25% for the collection of different database. Th e result shows that the proposed combined method outperforms than the existing of ICP or SCM in terms of detection time and accuracy in training.

[1]  Arun Ross,et al.  Towards understanding the symmetry of human ears: A biometric perspective , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[2]  Christoph Busch,et al.  Feature extraction from vein images using spatial information and chain codes , 2012, Inf. Secur. Tech. Rep..

[3]  Banafshe Arbab-Zavar,et al.  On guided model-based analysis for ear biometrics , 2011, Comput. Vis. Image Underst..

[4]  A. Ossowski,et al.  Example of human individual identification from World War II gravesite. , 2013, Forensic science international.

[5]  Mohammed Bennamoun,et al.  Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[6]  Thirimachos Bourlai,et al.  On ear-based human identification in the mid-wave infrared spectrum , 2013, Image Vis. Comput..

[7]  Wen Feng Lu,et al.  A Dental Matching Approach Using Partial Surface Features for Human Identification , 2012 .

[8]  Mark S. Nixon,et al.  On Shape-Mediated Enrolment in Ear Biometrics , 2007, ISVC.

[9]  Mohammed Bennamoun,et al.  Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition , 2007, International Journal of Computer Vision.

[10]  R. Vidal.  A TUTORIAL ON SUBSPACE CLUSTERING , 2010 .

[11]  Balaji Srinivasan,et al.  Ear Biometrics in Human Identification System , 2012 .

[12]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[13]  Singh Amarendra,et al.  Ear Recognition for Automated Human Identification , 2012 .

[14]  Mark S. Nixon,et al.  The ear as a biometric , 2007, 2007 15th European Signal Processing Conference.

[15]  Mohammed Bennamoun,et al.  A Fast and Fully Automatic Ear Recognition Approach Based on 3D Local Surface Features , 2008, ACIVS.