Abstract
Palmprint is a relatively new physiological biometric used in identification systems due to its stable and unique characteristics. The vivid texture information of palmprint present at different resolutions offers abundant prospects in personal recognition. This paper describes a new method to authenticate individuals based on palmprint identification. In order to analyze the texture information at various resolutions, we introduce a new hybrid wavelet, which is generated using two or more component transforms incorporating both their properties. A unique property of this wavelet is its flexibility to vary the number of components at each level of resolution and hence can be made suitable for various applications. Multi-spectral palmprints have been identified using energy compaction of the hybrid wavelet transform coefficients. The scores generated for each set of palmprint images under red, green and blue illuminations are combined using score-level fusion using AND and OR operators. Comparatively low values of equal error rate and high security index have been obtained for all fusion techniques. The experimental results demonstrate the effectiveness and accuracy of the proposed method.
Highlights
With increasing threat to security there is a grave need for identification in our society
The hybrid wavelet used here is a flexible wavelet which analyzes functions at all levels of resolutions where number of components at each level can be controlled and the wavelet itself can be varied by using different component transforms
The hybrid wavelet used here gives far better performance than the individual component transforms in various applications like image data compression, content-based image retrieval etc
Summary
With increasing threat to security there is a grave need for identification in our society. For palmprints the various approaches that can be used are: 1) Line-based approach: Palm lines are prominent and unique features of a palm. The extraction of these lines by using edge-based detection methods [3] and morphological operations [4] are quite common. Properties of many transforms can be combined in hybrid transform and the results are better than those obtained using single transform This was further extended to bi-resolution hybrid wavelets [18] which analyze palmprints at only global and local levels of resolution and were successfully used for identification purpose.
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