Fingerprint alignment using a two stage optimization
Pattern Recognition Letters
Singular point detection by shape analysis of directional fields in fingerprints
Pattern Recognition
Coarse fingerprint registration using orientation fields
EURASIP Journal on Applied Signal Processing
Nonminutiae-based decision-level fusion for fingerprint verification
EURASIP Journal on Applied Signal Processing
Vessel enhancement filter using directional filter bank
Computer Vision and Image Understanding
Personal Identification Using Palmprint and Contourlet Transform
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Consensus fingerprint matching with genetically optimised approach
Pattern Recognition
Multichannel texture segmentation using bamberger pyramids
Journal on Image and Video Processing
r-Theta and orientation invariant transform and signal combining for fingerprint recognition
Expert Systems with Applications: An International Journal
A novel contourlet based online fingerprint identification
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
A feature level multimodal approach for palmprint identification using directional subband energies
Journal of Network and Computer Applications
A novel verification criterion for distortion-free fingerprints
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Fingerprint image enhancement using decimation free directional adaptive mean filtering
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Enhancing face recognition using Directional Filter Banks
Digital Signal Processing
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The paper presents a new filter bank-based fingerprint feature extraction and matching method without needing to detect minutiae. The proposed method decomposes a fingerprint image into eight directional subband outputs using a directional filter bank (DFB) and then obtains directional energy distributions for each block from the decomposed subband outputs. Only dominant directional energy components are employed as elements of the input feature vector, which serves to reduce noise and improve efficiency. For the rotational alignment, additional input feature vectors in which various rotations are considered are extracted, and these input feature vectors are compared with the enrolled template feature vector. The proposed method significantly reduces the memory cost and processing time associated with verification, primarily because of the efficient DFB structure and the exploitation of directional specific information. Experimental results validate the effectiveness of the proposed method in extracting fingerprint features and achieving good performance.