Abstract: Biometric Recognition Method provides a greater recognition rate, superior effectiveness and makes the user operating more comfortable. Palm print recognition is considered the most feasible and consistent biometric recognition technique remaining to its merits such as low cost, user sociability with high speed and high accuracy. Advanced and fast correlation based feature for palm print recognition based on modified correlation filter classifier with spatial entities identifies more line features of the palm print very efficiently and in a stochastic manner but fails to adapt the texture variance. To overcome the above issue to implement a new technique termed Palm print Texture Recognition using the Connected-section Morphological Segmentation (PTR-CMS) for effective adaptation of texture variance and to remove the noise from palm print recognition. PTR-CMS technique is to reliably segment the images to smaller regions from the captured images. The proposed scheme is evaluated in terms of segmented regional texture variance based on the partition size and average equal error rate. Connected-section morphological segmentation (PTR-CMS) technique considers the problem of which fails to provide texture variance. An analytical and empirical result shows the lesser false acceptance rate with the efficient adaptation of the texture variance of our proposed scheme.
A. Kanchana and S. Arumugam, 2014. Palm Print Texture Recognition Using Connected-Section Morphological Segmentation. Asian Journal of Information Technology, 13: 119-125.