Fingerprint classification using a hexagonal fast fourier transform

Fitz, A.P.; Green, R.J.

doi:10.1016/0031-3203(96)00018-0

Cited by 83 publications

(33 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is even true for human finger prints (Prabhakar, 2001) although various classification paradigms of false acceptance or false rejection, examined the distinguishing nature of the physical characteristics. The prediction accuracies achieved for human finger prints typically lie between 85% (Fitz and Green, 1996) and 96.5% (Chong et al, 1997), using different data bases (such as singular points, orientation field, ridge lines) and paradigms (such as rules, supervised learning, discrete class assignments based on knowledge-based features; see Prabhakar, 2001). …”

Section: Discussionmentioning

confidence: 99%

Individuality of wing patterning in Giant honey bees (Apis laboriosa)

Kastberger

Radloff

Kranner³

2003

Apidologie

View full text Add to dashboard Cite

-We investigated whether individual worker bees of a single Apis laboriosa colony can be reidentified by their wing patterns alone. In a sample of 183 bees we pre-selected 100 workers belonging to 12 intra-colonial patrilines and re-identified them by 25 size-free wing characters. Re-identification was carried out by Self Organizing Map (SOM) reclassification and conventional discriminant analysis (DA) using the protocols of recognition (data for training and testing the model are equal or slightly modified by white noise), and prediction (test data are unknown to the model). SOM recognition of wing shaping was found to be more robust than that resulting from DA. If the test data were altered by white noise, SOM recognition success was 100% within a range of 3% modification which corresponded to the overall measurement error; under these conditions DA success was less than 40%. The SOM prediction capacity was tested using four test-training data ratios and reached 90% under a two-step reclassification protocol.individuality / wing pattern / SOM re-classification / Giant honeybee / Apis laboriosa

show abstract

Section: Discussionmentioning

confidence: 99%

Individuality of wing patterning in Giant honey bees (Apis laboriosa)

Kastberger

Radloff

Kranner³

2003

Apidologie

View full text Add to dashboard Cite

show abstract

“…Edge based methodologies customarily utilize the data of the structure recurrence of the unique finger impression edges for arrangement purposes. [15] Considers the recurrence range of fingerprints, A wedge-ring indicator is utilized to parcel the recurrence area pictures into noncovering zones in which the pixel qualities are summed up to structure a feature vector. When the peculiarity vector is discovered, it is contrasted with the reference characteristic vectors of each of the classes and a further order is performed by utilizing a closest neighbor arrangement system.…”

Section: IVmentioning

confidence: 99%

A Review of Wavelet Based Fingerprint Image Retrieval

Birdi¹

2015

IJRITCC

View full text Add to dashboard Cite

Abstract:A digital image is composed of pixels and information about brightness of image and RGB triples are used to encode color information. Image retrieval problem encountered when searching and retrieving images that is relevant to a user's request fr om a database. In Content based image retrieval, input goes in the form of an image. In these images, different features are extracted and then the other images from database are retrieved accordingly. Biometric distinguishes the people by their physical or behavioral qualities. Fingerprints are viewed as a standout amongst the most solid for human distinguishment because of their uniqueness and ingenuity. To retrieve fingerprint images on the basis of their textural features, by using different wavelets. From the input fingerprint image, first of all center point area is selected and then its textural features are extracted and stored in database. When a query image comes t hen again its center point is selected and then its texture feature are extracted. Then these features are matched for similarity and then resultant image is displayed.

show abstract

“…We look for a shear operating along the first vector of this lattice, thus with matrix where has the form (15) At the end, the lattice has matrix , or, equivalently (16) The last factorization (16) provides the geometric interpretation of the decomposition, using shears along 1-D directions of the supporting lattices. On the other hand, the factorization (11), when read from the left to the right, provides the practical way for implementing the conversion between the two lattices, as detailed in the next section, since the operations are directly expressed in the basis of the pixel indices.…”

Section: A Mathematical Principlementioning

confidence: 99%

Reversible, Fast, and High-Quality Grid Conversions

Condat

Ville

Forster-Heinlein

2008

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-A new grid conversion method is proposed to resample between two 2-D periodic lattices with the same sampling density. The main feature of our approach is the symmetric reversibility, which means that when using the same algorithm for the converse operation, then the initial data is recovered exactly. To that purpose, we decompose the lattice conversion process into (at most) three successive shear operations. The translations along the shear directions are implemented by 1-D fractional delay operators, which revert to simple 1-D convolutions, with appropriate filters that yield the property of symmetric reversibility. We show that the method is fast and provides high-quality resampled images. Applications of our approach can be found in various settings, such as grid conversion between the hexagonal and the Cartesian lattice, or fast implementation of affine transformations such as rotations.

show abstract

Fingerprint classification using a hexagonal fast fourier transform

Cited by 83 publications

References 4 publications

Individuality of wing patterning in Giant honey bees (Apis laboriosa)

Individuality of wing patterning in Giant honey bees (Apis laboriosa)

A Review of Wavelet Based Fingerprint Image Retrieval

Reversible, Fast, and High-Quality Grid Conversions

Contact Info

Product

Resources

About