A gradient-based weighted averaging method for estimation of fingerprint orientation fields

Estimation of orientation fields is an essential module in a fingerprint recognition system. Conventional gradient based approaches are popular but very sensitive to noise. In this paper, we propose a new implementation that is more resistant to noise. Our basic idea is to conduct redundant estimation over four overlapping neighborhoods for each target block. Following this idea, we devise a weighted averaging scheme operated on the base blocks directly. Thus, each block (including the target one) in the overlapping neighborhoods has different impact on estimation of the dominant orientation fields. Our preliminary experiment results suggest that the proposed weighted averaging algorithm is more robust against noise in comparison with other gradient based methods

Determining the Standard Value of Acquisition Distortion of Fingerprint Images Based on Image Quality

This paper describes a novel procedure for determining the standard value of acquisition distortion of fingerprint images. Knowledge about the standard value of acquisition distortion of the fingerprint images is very important in determining the method for improving image quality. In this paper, we propose a model to determine the standard value that can be used in classifying the type of distortion of the fingerprint images based on the image quality. The results show that the standard value of acquisition distortion of the fingerprint images based on the image quality have values of the local clarity scores (LCS) follows: dry parameter values are in the range of 0.0127-0.0149, neutral parameter values are less than 0.0127, and oily parameter values are greater than 0.0149. Meanwhile, the global clarity scores (GCS) are as follows: dry parameter values are in the range of 0.0117-0.0120, neutral parameter values are less than 0.0117, and oily parameter values are greater than 0.0120; and ridge-valley thickness ratios (RVTR) are as follows: dry parameter values are less than 7.75E-05, neutral parameter values are 7.75E-05-5.94E-05, and oily parameter values are greater than 5.94E-05

MINIMIZING DISTORTION IN STEGANOG-RAPHY BASED ON IMAGE FEATURE

There are two defects in WOW. One is image feature is not considered when hiding information through minimal distortion path and it leads to high total distortion. Another is total distortion grows too rapidly with hidden capacity increasing and it leads to poor anti-detection when hidden capacity is large. To solve these two problems, a new algorithm named MDIS was proposed. MDIS is also based on the minimizing additive distortion framework of STC and has the same distortion function with WOW. The feature that there are a large number of pixels, having the same value with one of their eight neighbour pixels and the mechanism of secret sharing are used in MDIS, which can reduce the total distortion, improve the antidetection and increase the value of PNSR. Experimental results showed that MDIS has better invisibility, smaller distortion and stronger anti-detection than WOW

[[alternative]]Design of a Fingerprint Classification System

計畫編號：NSC94-2213-E032-020研究期間：200508~200607研究經費：536,000[[abstract]]在生物辨識研究的範疇裡，指紋可以算是目前最廣為應用的生物特徵之一，也因為指紋被如此廣泛應用及建檔，指紋資料庫規模日益龐大，因此，為了減少在龐大的指紋資料庫中搜尋的時間，以減少系統運算量，我們通常會先將指紋作一初步的分類。指紋分類可視為指紋辨識的粗略比對程序，用以剔除差異性過大的樣本，在過去的研究文獻中，已提出許多種有效的指紋分類的方法，這些方法各有其優缺點，其中奇異點的搜尋易受雜訊影響，而且許多演算法速度上表現的並不傑出。本研究計畫中擬設計出一個有效且快速的分類方法。在本系統中，我們計畫直接透過在自動指紋辨識系統(AFIS)中細化後的影像擷取指紋方向的資訊以免除重複的影像強化步驟來提昇整體系統效能，並設計一個快速的區域中心搜尋演算法，找出我們所感興趣的區域中心，接著我們試圖分析中心周圍的方向資訊，找出每一類型指紋所代表的方向特徵，再將其特徵擷取下來作為分類依據，而且這些特徵將來也可以作為身份辨識的延伸資訊。最後我們擬以此研究領域中被廣為使用的NIST-4資料庫來訓練、測試我們的系統。[[sponsorship]]行政院國家科學委員

Fingerprint recognition through circular sampling

The uniqueness of the human fingerprint is considered to be one of the most reliable characteristics for personal identification. Considering that we leave them on nearly all of the surfaces we touch, they become valuable to those in law enforcement for identifying perpetrators of a crime. However, the matching of a single fingerprint with the millions that have been cataloged proves to be a difficult task. This study presents an alternate method to fingerprint recognition by way of a spatial re-sampling of the pattern through concentric circles. With this approach, the concentric circular samples have rotation invariant features while a translation is dependent only on the location of the circles\u27 center. The resulting circles are then correlated with those from the known set to obtain a collection of the most probable matches. This technique has shown exceptional results when comparing various binary test patterns as well as synthetic binary fingerprint images but is unable to recognize unenhanced greyscale fingerprint images

Fingerprint minutiae filtering based on multiscale directional information

Automatic identification of humans based on their fingerprints is still one of the most reliable identification methods in criminal and forensic applications, and is widely applied in civil applications as well. Most automatic systems available today use distinctive fingerprint features called minutiae for fingerprint comparison. Conventional feature extraction algorithm can produce a large number of spurious minutiae if fingerprint pattern contains large regions of broken ridges (often called creases). This can drastically reduce the recognition rate in automatic fingerprint identification systems. We can say that for performance of those systems it is more important not to extract spurious (false) minutia even though it means some genuine might be missing as well. In this paper multiscale directional information obtained from orientation field image is used to filter those spurious minutiae, resulting in multiple decrease of their number