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Palmprint identification refers to searching in a database for the palmprint template, which is from the same palm as a given palmprint input. The identification process involves preprocessing, feature extraction, feature matching and decision-making. As a key step in the process, in this paper, we propose a new feature extraction method by converting a palmprint image from a spatial domain to a frequency domain using Fourier Transform. The features extracted in the frequency domain are used as indexes to the palmprint templates in the database and the searching process for the best match is conducted by a layered fashion. The experimental results show that palmprint identification based on feature extraction in the frequency domain is effective in terms of accuracy and efficiency.

This paper proposes a score-based indexing technique for biometric databases. A fixed-length index is computed for each image in the database by calculating its match scores against a preselected set of representative images. Further, an efficient storage mechanism (i.e. index space) is developed to arrange the biometric images like traditional database records so that a rapid search is possible. During identification, the retrieval technique finds a list of similar candidates for the query image from the database using voting scheme. Finally, to identify the genuine match from the retrieved similar candidates, we perform a one-one match between the query and each similar candidate using modified Hausdorff distance measure. Experimental results on different databases show a significant performance improvement in terms of response time and identification accuracy compared to the existing indexing methods.