Fast Separable Gabor Filter for Fingerprint Enhancement

The new algorithm of unique point localization for fingerprint registration is proposed. The focal point, an average center of curvature of fingerprint, is the unique and reliable reference point, suitable for fingerprint registration. In this paper, the new approach demonstrates fast and reliable reference point. Moreover, the focal point can be located even it is out of original fingerprint boundary and singularity is not exist.

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“…Step 2: Enhance fingerprint image by using real directional separable Gabor filter in [9], and then binarize enhanced image.…”

Section: The New Focal Point Localization Algorithmmentioning

confidence: 99%

The New Focal Point Localization Algorithm for Fingerprint Registration

Areekul

Suppasriwasuseth

Jirachawang

2006

18th International Conference on Pattern Recognition (ICPR'06)

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“…The second phase performs convolution with an 1-D Gabor low-pass filter for all pixels in the block, total W 2 pixels, along ridge direction. Implementation detail is clearly explained in [3]. Since all Gabor filters are symmetry, filtering requires only ⎣L/2⎦ additions and ⎣L/2⎦+1 multiplications.…”

Section: Filtering Processmentioning

confidence: 99%

“…1-D band-pass Gabor filtering for 8 directions (SG8) [3] is analyzed. This algorithm is composed of three processes.…”

Section: Spatial Gabor Filtering Approachmentioning

confidence: 99%

“…The fingerprint image is partitioned into blocks of 16×16 pixels, and a basic segmentation scheme using mean and variance is employed. Five fingerprint enhancement filtering types are evaluated as follows; Traditional Gabor filtering with non-quantized orientation (TG) [2], Separable Gabor filtering with 8-quantized orientation (SG8) [3], Separable Gabor filtering with non-quantized orientation (SG) [4], Short Time Fourier Transform approach (STFT) [5], and Discrete Cosine Transform approach (DCT) [6]. In the spatial domain approaches, the discrete Gabor filters are the same 25×25 fixed-window size.…”

Section: Step 1) Initial Sequence Alignmentmentioning

confidence: 99%

“…This method employs a set of pre-computed Gabor filters that selected by local ridge orientation and local ridge frequency in order to perform 2-D convolution with an entire image. In addition, computational complexity of this Gabor filtering can be reduced by applying separable Gabor filters as reported in [3,4]. In frequency-domain approaches, Gabor filter can be implemented in Short Time Fourier Transform (STFT) domain, resulting in better enhancement around high curvature zone [5].…”

Section: Introductionmentioning

confidence: 99%

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Performance and Computational Complexity Comparison of Block-Based Fingerprint Enhancement

Jirachaweng

Leelasawassuk

Areekul

2009

Advances in Biometrics

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Abstract. Performance and computational complexity comparisons of various block-based fingerprint enhancement schemes are tested and reported in this literature. Enhancement performance is evaluated by comparing equal error rates, which obtained by a proposed fingerprint matching algorithm using local and global features. Various enhancement methods are tested; i.e. three types of spatial Gabor filtering, short-time Fourier transform filtering, and discrete cosine transform filtering. These enhancement schemes also tested with various databases such as FVC2000, FVC2002, and FVC2004. Finally, computational complexity of enhancement implementation is analyzed and concluded.

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