Fingerprint quality assessment based on wave atoms transform

Fingerprint image quality is of great importance for an automatic fingerprint identification system AFIS, and affects its performance. In this paper, a new fingerprint image quality measure based on wave atoms transform is proposed. Indeed, fingerprint features are extracted by exploiting wave atoms multi-scale and multi-directional properties. Hence, for good quality block, the singularities are concentrated in a same directional subbands. In the opposite, for poor quality, the singularities are scattered over several directional subbands. In order to evaluate the performance of the algorithm, FVC 2002 databases have been considered. The results show that this method has a serious potential in fingerprint quality evaluation and will improve the performance and effectiveness of AFIS.

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