Fingerprint image postprocessing: A combined statistical and structural approach

“…No feature extraction stage is described. Xiao and Raafat (9) assume that the skeleton image has already been derived from the ngerprint images. They describe methods to identify spurious minutiae and eliminate them using the structural de nition of minutiae.…”

Section: Background and Related Workmentioning

confidence: 99%

Adaptive flow orientation-based feature extraction in fingerprint images

Ratha

Chen

Jain

1995

Pattern Recognition

446

221

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“…However, the same method can be applied with locally adaptive thresholds. Other more advanced approaches include ridge/valley edge detection techniques using Laplacian operators as in Xiao & Raafat (1991), and mathematical morphology in Gonzalez & Woods (2007). Once produced, the binary image usually undergoes a morphological thinning operation, where ridge structures are reduced to 1-pixel thickness, referred to as the skeleton , in order to aid minutiae detection.…”

Section: Base Theory 21 Minutiae Extractionmentioning

confidence: 99%

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

Kwan¹,

Gao²

2011

State of the Art in Biometrics

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“…Here, we shall evaluate the performance of the composed prototype algorithm in an indirect way, by measuring to what extent the minutiae in the fingerprint structure can be captured, using a simple algorithm for minutiae extraction consisting of the following steps [41], [11], [42]:…”

Section: Experiments and Evaluation: IImentioning

confidence: 99%

Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection

Almansa

Lindeberg

2000

IEEE Trans. on Image Process.

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This work presents two mechanisms for processing fingerprint images; shape-adapted smoothing based on second moment descriptors and automatic scale selection based on normalized derivatives.The shape adaptation procedure adapts the smoothing operation to the local ridge structures, which allows interrupted ridges to be joined without destroying essential singularities such as branching points and enforces continuity of their directional fields. The scale selection procedure estimates local ridge width and adapts the amount of smoothing to the local amount of noise. In addition, a ridgeness measure is defined, which reflects how well the local image structure agrees with a qualitative ridge model, and is used for spreading the results of shape adaptation into noisy areas. The combined approach makes it possible to resolve fine scale structures in clear areas while reducing the risk of enhancing noise in blurred or fragmented areas. The result is a reliable and adaptively detailed estimate of the ridge orientation field and ridge width, as well as a smoothed grey-level version of the input image.We propose that these general techniques should be of interest to developers of automatic fingerprint identification systems as well as in other applications of processing related types of imagery.

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Fingerprint image postprocessing: A combined statistical and structural approach

Cited by 100 publications

References 3 publications

Adaptive flow orientation-based feature extraction in fingerprint images

Adaptive flow orientation-based feature extraction in fingerprint images

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection

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