A hybrid system for automatic fingerprint identification

Huvanandana, Sanpachai; Malisuwan, Settapong; Santiyanon, J.; Hwang, Jenq‐Neng

doi:10.1109/iscas.2003.1206133

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…8. Examples of poor quality fingerprint images due to: noisy acquisition device (a), (b) and variation in impression conditions (c), (d), resulting in corrupted ridgelines Ko (2002) and Sherlock et al (1994) suggested gray-scale image enhancement techniques, which are applied in the frequency domain, while Hong et al (1998) and Huvanandana et al (2003) employed their techniques directly in the space domain. Cheng et al (2002) applies scale space theory to fingerprints enhancement by filtering the image in an iterative manner using both local and global image charackteristics.…”

Section: Fig 7 Examples Of Minutiae (Ridge Ending and Bifurcation) mentioning

confidence: 99%

Anisotropic Filtering Techniques applied to Fingerprints

Greenberg¹,

Kogan²

2007

Vision Systems: Segmentation and Pattern Recognition

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Section: Fig 7 Examples Of Minutiae (Ridge Ending and Bifurcation) mentioning

confidence: 99%

Anisotropic Filtering Techniques applied to Fingerprints

Greenberg¹,

Kogan²

2007

Vision Systems: Segmentation and Pattern Recognition

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“…The iris images used in this experiment come from CASIA database [5]. The detail of iris recognition algorithm that has been used in this paper is adopted from [6]. This research introduces image enhancement techniques and discusses the implementation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Study of Image Enhancement for Iris Recognition

Sanpachai¹,

Malisuwan²

2014

JIII

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Iris recognition is one of the most prevalent methods for personal identification in the modern day. Low quality iris images that are blurry, of low resolution, and poor illumination poses a big challenge for iris recognition as the iris recognition efficiency is entirely dependent on whether the image supplied is of good quality. Therefore, several enhancement techniques have been proposed and used for image processing to increase the quality of iris images. Although there is no best approach for all types of image enhancement, histogram equalization (HE) is a commonly used approach as it is a simple yet effective method. In this paper, several enhancement techniques based on histogram equalization approaches, such as histogram equalization (HE), brightness preserving bihistogram equalization (BBHE), dynamic histogram equalization (DHE), adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), dualistic sub-image histogram equalization (DSIHE), and multi-scale adaptive histogram equalization (MAHE), have been explored. A comparison is conducted based on the image quality of all image enhancement techniques proposed in this paper. 

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“…Using fast Fourier transform (FFT) and non‐discriminant analysis, Park and Park [14] developed a fingerprint identification system. An advanced hybrid biometric system was realised using ridge orientation, texture feature and minutiae [15]. Using registered directional field estimate, finger code and minutiae triplets a fingerprint image classification system was developed by de Boer et al [16].…”

Section: Introductionmentioning

confidence: 99%

New approach for identifying hereditary relation using primary fingerprint patterns

Karthikeyini

Rajamani²,

Kanagaraj³

2013

IET Image Processing

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In this work, an effort has been made to explore the existence of hereditary relations by analysing the central region of the fingerprints using global ridge patterns. So far, fingerprint identification systems have been developed for biometric applications and criminal investigations. The present work attempts to identify the hereditary relation among inter and intra class family members using fingerprints. Fingerprints are obtained from 324 subjects of 54 families comprising of a group of three generations. Ridge pattern types and ridge orientation map are estimated for the analysis. The results obtained show that 85.18% of intra class family members have similar ridge pattern. The study on ridge orientation map indicates that the angles of ridge orientation field vary in the same way for intra class family members. Furthermore, the specific range of angles of ridge orientation field occurs maximum number of times for intra class members and location of median value of repetitions of orientation angles are the same. Hence, it is evident that the intra class members have certain similarities in fingerprint which in turn replicate the presence of hereditary relation.

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A hybrid system for automatic fingerprint identification

Cited by 5 publications

References 9 publications

Anisotropic Filtering Techniques applied to Fingerprints

Anisotropic Filtering Techniques applied to Fingerprints

A Study of Image Enhancement for Iris Recognition

New approach for identifying hereditary relation using primary fingerprint patterns

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