Comparative Analysis of Fingerprint Features Extraction Methods

Mua’ad M. Abu-Faraj, Ziad A. Alqadi, Khaled Aldebei


Human fingerprint features extraction is important for building a fingerprint classification system. The extracted features must be unique and stable by remaining unchanged for the same fingerprint with various rotation degrees. This paper will analyze the most famous methods used to extract fingerprint features. K-means, LBP, WPT, and minutiae methods will be investigated; the obtained experimental results will be analyzed and compared to raise suitable recommendations for using these methods. The process of extracting the fingerprint's characteristics is one of the important things for use in discrimination systems, which requires saving memory and accelerating the classification process to take the appropriate decision and as quickly as possible. The stability of the properties of the fingerprint image, regardless of the different rotations, will lead to the use of a single features vector for the fingerprint with its different conditions and thus treat all the rotated fingerprints as one fingerprint image. This factor will be studied in this research to recommend the experts interested in building fingerprint recognition systems.


Keywords: fingerprint, features, local binary pattern, K-means, wavelet packet tree, bifurcation, ridge, Euclidean distance.

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