Palmprint authentication using fusion of wavelet and contourlet features

Low resolution palmprint images consist of discriminative multisized and multidirectional principal lines and wrinkles. Intuitively, discrete wavelet transform (DWT) is a good choice to extract such patterns due to its space-frequency localization, multiresolution analysis (MRA) capability, and computational efficiency. However, most of the DWT-based palmprint recognition systems fail to report low equal error rate (EER) due to inherent limitations of DWT and shift-rotational variations in the intraclass palmprint images. This paper proposes the techniques for shift and rotation invariant feature extraction using DWT extension. The effectiveness of these techniques is tested on deliberately shifted and rotated palmprints. Further, limited directionality due to DWT is overcome by augmenting with features of contourlet transform. Contourlet transform can extract curve singularities effectively with multidirectional decomposition capability; wavelets are good in extracting point singularities. The different views of contourlet transform and DWT on palmprints motivate us to extract contourlet and wavelet features, and examine them for their individual and combined verification performances. The combined mode is found to perform well over their individual performances. The average EER (0.41%), obtained on PolyU-Online-Palmprint-Database-II (PolyU), is better than the existing wavelets/transform-based palmprint recognition approaches and comparable to the other state of the art palmprint recognition approaches. The computational burden on feature extraction and matching is substantially low thereby making the approach suitable for resource constrained environments. Copyright (C) 2010 John Wiley & Sons, Ltd.