Biomaterial Database

Fingerprint image enhancement using CNN filtering techniques. 2003

Ertugrul Saatci, and Vedat Tavsanoglu

Faculty of Engineering, Science and The Built Environment, London South Bank University, Borough Road, SE1 0AA, London, UK. saatcie@lsbu.ac.uk.

Due to noisy acquisition devices and variation in impression conditions, the ridgelines of fingerprint images are mostly corrupted by various kinds of noise causing cracks, scratches and bridges in the ridges as well as blurs. These cause matching errors in fingerprint recognition. For an effective recognition the correct ridge pattern is essential which requires the enhancement of fingerprint images. Segment by segment analysis of the fingerprint pattern yields various ridge direction and frequencies. By selecting a directional filter with correct filter parameters to match ridge features at each point, we can effectively enhance fingerprint ridges. This paper proposes a fingerprint image enhancement based on CNN Gabor-Type filters.

UI	MeSH Term	Description	Entries
D007089	Image Enhancement	Improvement of the quality of a picture by various techniques, including computer processing, digital filtering, echocardiographic techniques, light and ultrastructural MICROSCOPY, fluorescence spectrometry and microscopy, scintigraphy, and in vitro image processing at the molecular level.	Image Quality Enhancement,Enhancement, Image,Enhancement, Image Quality,Enhancements, Image,Enhancements, Image Quality,Image Enhancements,Image Quality Enhancements,Quality Enhancement, Image,Quality Enhancements, Image
D003878	Dermatoglyphics	The study of the patterns of ridges of the skin of the fingers, palms, toes, and soles.	Fingerprints,Plantar Prints,Fingerprint,Plantar Print,Print, Plantar,Prints, Plantar
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron