Principal component analysis for ear-based biometric verification

Resumen

In this work, the Curvelet transform is proposed as a fairly new feature extraction method for palmprint recognition. Particularly, a multiscale analysis has been performed at four levels, assessing and combining the features extracted at each level in order to find those which better represent the palmprint. Feature matching has been conducted by means of Euclidean distance and Support Vector Machines (SVMs), and comparative results are provided. In addition, a multimodal approach involving the extracted palmprint features and hand geometry features has also been evaluated, obtaining an improvement of the results in relation to monomodal biometrics. Evaluations have been carried out following an evaluation protocol based on the definitions suggested by the ISO/IDE 19795 norm that allows for a fair comparison between the different methods. To this end, images coming from two different contact-less databases, which cover different capturing conditions, have been employed.