论文标题
指纹特征提取通过使用多任务CNN组合纹理，细节和频谱来提取。
Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN
论文作者
论文摘要
尽管大多数指纹匹配方法都利用细节点和/或指纹图像的纹理作为指纹特征，但频谱也是一个有用的功能，因为指纹由带有其固有频带的山脊图案组成。我们提出了一种基于CNN的新方法，用于从纹理，细节和频谱中提取指纹特征。为了从细节周围的当地区域提取有效的纹理特征，将细节注意模块引入了建议的方法。我们还提出了新的数据增强方法，该方法考虑了指纹图像的特征，以增加培训期间图像的数量，因为我们仅在培训中使用公共数据集，其中包括一些指纹类。通过使用FVC2004 DB1和DB2进行的一组实验，我们证明了所提出的方法与商业指纹匹配软件和常规方法相比，在指纹验证方面表现出有效的性能。
Although most fingerprint matching methods utilize minutia points and/or texture of fingerprint images as fingerprint features, the frequency spectrum is also a useful feature since a fingerprint is composed of ridge patterns with its inherent frequency band. We propose a novel CNN-based method for extracting fingerprint features from texture, minutiae, and frequency spectrum. In order to extract effective texture features from local regions around the minutiae, the minutia attention module is introduced to the proposed method. We also propose new data augmentation methods, which takes into account the characteristics of fingerprint images to increase the number of images during training since we use only a public dataset in training, which includes a few fingerprint classes. Through a set of experiments using FVC2004 DB1 and DB2, we demonstrated that the proposed method exhibits the efficient performance on fingerprint verification compared with a commercial fingerprint matching software and the conventional method.
