Abstract
This paper presents an improved image-based fingerprint verification system. The proposed system enhances an input fingerprint image using a contextual filtering technique in the frequency domain, and uses the complex fillers to identify the core point. Subsequently, a region of interest (ROI) of a predefined size, which is centered around the detected core point, is extracted. The resulting ROI is rotated based on the detected core point angle to ensure rotation invariance. The proposed system extracts the absolute average deviation from the outputs of eight oriented Gabor filters that are applied to the ROI. To reduce the dimensionality of the extracted features whilst generating more discriminatory representation, this paper compares the unsupervised principal component analysis and the supervised linear discriminant analysis methods for dimensionality reduction. User-specific thresholding schemes are investigated. The effectiveness of the proposed algorithm is evaluated on the public FVC2002 set_a database. Experimental results demonstrate the superiority of the introduced solution in comparison with existing approaches.
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Ibrahim, M.T., Wang, Y., Guan, L., Venetsanopoulos, A.N. (2011). Fingerprint Verification Using Rotation Invariant Feature Codes. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_12
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DOI: https://doi.org/10.1007/978-3-642-21596-4_12
Publisher Name: Springer, Berlin, Heidelberg
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