A Filter Bank Based Approach for Rotation Invariant Fingerprint Recognition

摘要

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 invari-ance. 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 un-supervised 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. Complex filter; Contextual filtering; Gabor filters; Average absolute deviation (AAD); Linear