S. M. Prasad
ABSTRACT
Presence of unique palmprint patterns makes palmprints suitable for personal recognition. In low resolution images, these patterns mainly consist of multisized and multidirectional principal lines and wrinkles. This prompted us to use wavelet based multiresolution analysis (MRA) to extract these features. Most of the palmprint feature extraction works based on wavelets fail to consider spatial location of energies. We have proposed a new method to extract these features in the form of spatially localized wavelet energy signatures (SLWES) which comprise spatial and frequency information at different resolutions. SLWES features characterize palmprints effectively and are represented in a vector form. We have also identified a few new hand geometrical features suitable for personal recognition, and thirty hand geometry features (including new) are extracted to form a geometrical feature vector. These feature vectors are then examined for their individual and combined (fusion) identification performances. We experimented these methods on our database and obtained 97.5% accuracy for combined mode, which is comparable with similar bimodal (geometrical and palmprints) identification methods.
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Index Terms
- Bimodal personal recognition using hand images
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