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    梅 园 孙怀江 夏德深. 一种基于梯度的健壮的指纹方向场估计算法[J]. 计算机研究与发展, 2007, 44(6): 1022-1031.
    引用本文: 梅 园 孙怀江 夏德深. 一种基于梯度的健壮的指纹方向场估计算法[J]. 计算机研究与发展, 2007, 44(6): 1022-1031.
    Mei Yuan, Sun Huaijiang, and Xia Deshen. A Gradient-Based Robust Method for Estimation of Fingerprint Orientation Field[J]. Journal of Computer Research and Development, 2007, 44(6): 1022-1031.
    Citation: Mei Yuan, Sun Huaijiang, and Xia Deshen. A Gradient-Based Robust Method for Estimation of Fingerprint Orientation Field[J]. Journal of Computer Research and Development, 2007, 44(6): 1022-1031.

    一种基于梯度的健壮的指纹方向场估计算法

    A Gradient-Based Robust Method for Estimation of Fingerprint Orientation Field

    • 摘要: 作为指纹的全局特征,指纹方向场在自动指纹识别系统中发挥了非常重要的作用.提出了一种基于梯度的健壮的指纹方向场估计算法,新算法首先归一化点梯度向量并计算块梯度向量及相应的块一致性;然后估计噪声区域;最后采用基于迭代的方法,重新估计所有块梯度向量并将梯度向量场转化为方向场.实验结果表明,与已有基于梯度的指纹方向场估计算法相比,新算法具有更高的准确性及抗噪性能,并能较好地估计大块噪声内的方向场,是一种较为健壮的指纹方向场估计算法.

       

      Abstract: Automatic fingerprint identification system (AFIS), which is one of the most important biometric authentication, has been extensively studied and good performance on small database is obtained, but there still exist some critical issues such as long processing time on large databases and low matching rate on poor image. To solve these problems, improvements of fingerprint classification and identification are needed. As a global feature of fingerprint, orientation field which describes the local direction of the ridge-valley pattern plays a very important role in both topics mentioned above. Many fingerprint orientation estimating methods based on gradient have been proposed, but their results are not very satisfactory, especially for poor images. In this paper, a gradient based robust method for estimation of fingerprint orientation fields is proposed. This new method mainly comprises three steps: firstly, normalize the point-gradient vectors and calculate the block-gradient vectors and the corresponding block-coherence; then detect the noisy areas to eliminate the side effect of noise diffusing; finally, re-estimate all block-gradient vectors based on iteration and transform the gradient field to orientation field. Compared with the previously proposed gradient-based methods, experiments conducted on FVC 2000 and FVC 2004 show that the proposed method is more accurate and more robust against noise, and is able to predict orientation field within the large noisy areas.

       

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