Fingerprint pattern classification
Pattern Recognition
A Real-Time Matching System for Large Fingerprint Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Identity Authentication Using Fingerprints
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint classification and matching using a filterbank
Fingerprint classification and matching using a filterbank
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
Fingerprint enhancement using STFT analysis
Pattern Recognition
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 0.00 |
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.