A human ear recognition method using nonlinear curvelet feature subspace

Abstract

Ear is a relatively new biometric among others. Many methods have been used for ear recognition to improve the performance of ear recognition systems. In continuation of these efforts, we propose a new ear recognition method based on curvelet transform. Features of the ear are computed by applying Fast Discrete Curvelet Transform via the wrapping technique. Feature vector of each image is composed of an approximate curvelet coefficient and second coarsest level curvelet coefficients at eight different angles. k-NN (k-nearest neighbour) is utilized as a classifier. The proposed method is experimented on two ear databases from IIT Delhi. Results achieved using the proposed method on publicly available ear database are up to 97.77% which show encouraging performance.

Keywords:

• ear recognition
• curvelet transform
• ear biometric
• segmentation
• feature extraction

2010 AMS Subject Classifications:

• 68U10
• 62H30
• 68T10
• 68T45

Acknowledgements

The authors thank the Biometrics Research Laboratory at IIT Delhi for providing access to public biometric ear databases on request. Authors also thank Dr Ibrahima Faye and the anonymous reviewers for their valuable suggestions which helped to improve the manuscript.