Abstract
This paper presents a systematic approach for image-based fingerprint recognition. The proposed method first enhances an input fingerprint image using a contextual filtering based method in the frequency domain. Complex filters are used for the detection of the core point, and a region of interest (ROI) of a predefined size centered at the detected core point is extracted. The resulting ROI is rotated based on the angle of the detected core point to ensure rotation invariance. Subsequently, the proposed system extracts the average absolute deviation (AAD) from the outputs of a Gabor filter bank. To reduce the dimensionality of the extracted features whilst generating more discriminatory representation, this paper compares the unsupervised Principal Component Analysis (PCA) and the supervised Linear Discriminant Analysis (LDA) methods for dimensionality reduction. User-specific thresholding schemes are investigated to improve the verification performance. The effectiveness of the proposed method is demonstrated through extensive experimentation on the FVC2002 set_a public database, in both identification and verification scenarios.
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Ibrahim, M.T., Wang, Y., Guan, L. et al. A Filter Bank Based Approach for Rotation Invariant Fingerprint Recognition. J Sign Process Syst 68, 401–414 (2012). https://doi.org/10.1007/s11265-011-0630-x
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DOI: https://doi.org/10.1007/s11265-011-0630-x