Abstract
Fingerprint enhancement is a key step in the Automated Fingerprint Identification System. Because of poor quality of a fingerprint the algorithm for feature extraction may extract features incorrectly, which affects incorrect fingerprint match and consequently inefficient fingerprint-based identity verification. Fingerprint image enhancement techniques are based on enhancement in spatial domain or in frequency domain or in a combination of both. This article presents a block–local normalization algorithm and a technique for speeding up a two-stage algorithm for low-quality fingerprint image enhancement with image learning, which first enhances a fingerprint image in the spatial domain and then in the frequency domain. The normalization technique includes an algorithm with block–local normalization with different block sizes. Experimental results obtained on a public database FVC2004 showed that the presented normalization technique speeds up and improves a state-of-the-art two-stage algorithm, provides better results in comparison with global and local normalization, and positively affects fingerprint image enhancement, and consequently improves the efficiency of the automated fingerprint identification system.
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