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.

Fingerprint image enhancement is a key step in the Automated Fingerprint Identification System (AFIS). Because of different factors that affect the image, such as skin condition (very dry or moist, damaged or worn down skin, etc.), sensor noise, irregular print on the sensor, etc., the fingerprint image needs to be enhanced so that the structures of ridges and valleys are clearly visible. This paper presents fingerprint image enhancement with oriented linear anisotropic diffusion in the first stage and oriented local ridge compensation in the second stage. To control the process of oriented diffusion we have determined an orientation field from the previously established ridge orientation, which was additionally enhanced. Because the overall image contrast is decreased after the diffusion process, we have enhanced the contrast with block local normalization. In the second stage we have additionally enhanced ridge structure with oriented local ridge compensation. We have compared and combined our proposed algorithm with some of the state-of-the-art algorithms. The results of experiments, done on a public database FVC2004, show efficient fingerprint image enhancement.

Fingerprint image enhancement is of key importance for the efficiency of the automated fingerprint identification system. Before we can enhance a fingerprint image with contextual filters, we need to enhance fingerprint image contrast and readability with non-directional filters. This article provides an analysis of the influence of non-directional image enhancement techniques in the fingerprint image preprocessing step. To perform the analysis we used global normalization algorithm, local normalization algorithm, Wiener filter, histogram equalization algorithm and median filter. To evaluate the equal error rate in the experiments, done on a public database FVC_2004, we used Gabor filter and a state-ofthe-art two-stage algorithm.

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