Abstract Conventional minutia-based fingerprint recognition requires a complicated geometric matching and hard to be adopted in the bit-string based cancellable biometrics or bio-encryption, as the minutia data representing a fingerprint image is geometrical, unordered and variable in size. In this paper, we propose a new method to represent a fingerprint image by an ordered and fixed-length bit-string to cope with those difficulties with providing a faster matching, compressibility and improved accuracy performance as well. Firstly, we devised a novel minutia-based local structure modeled by a mixture of 2D elliptical Gaussian functions to represent a minutia in the image pixel space. Then, each local structure was mapped to a point in a Euclidean space by normalizing the local structure by the number of minutiae in it. This simple yet crucial computation for converting the image space to the Euclidean-space enabled the fast dissimilarity computation of two local structures and all followed processes in our proposed method. A complementary texture-based local structure to the minutia-based local structure was also introduced, whereby both were compressed via principal component analysis and fused in the compressed Euclidean space. The fused local structures were then converted to a K-bit ordered string using the K-means clustering algorithm. This chain of computations with the sole use of Euclidean distance was vital for speedy and discriminative bit-string conversion. The accuracy was further improved by the finger-specific bit-training algorithm, in which two criteria were leveraged to select the useful bit positions for matching. Experiments were performed on Fingerprint Verification Competition (FVC) databases for comparisons with the existing techniques to show the superiority of the proposed method.

中文翻译：

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通过归一化局部结构对指纹图像的位串表示

摘要 传统的基于细节的指纹识别需要复杂的几何匹配，并且难以在基于位串的可取消生物特征或生物加密中采用，因为代表指纹图像的细节数据是几何的、无序的、大小可变的。在本文中，我们提出了一种通过有序和固定长度的位串来表示指纹图像的新方法，以解决这些困难，同时提供更快的匹配、可压缩性和改进的精度性能。首先，我们设计了一种新颖的基于细节的局部结构，该结构由 2D 椭圆高斯函数的混合建模，以表示图像像素空间中的细节。然后，通过将局部结构按其中的细节数量归一化，将每个局部结构映射到欧几里得空间中的一个点。这种将图像空间转换为欧几里得空间的简单而重要的计算使我们能够在我们提出的方法中快速计算两个局部结构和所有后续过程。还引入了与基于细节的局部结构互补的基于纹理的局部结构，通过主成分分析对两者进行压缩并在压缩的欧几里得空间中融合。然后使用 K 均值聚类算法将融合的局部结构转换为 K 位有序字符串。这一系列仅使用欧几里得距离的计算链对于快速和有区别的位串转换至关重要。手指特定的比特训练算法进一步提高了准确性，其中利用两个标准来选择有用的比特位置进行匹配。