Minutiae, widely used feature points of fingerprint images, directly decide the performance of fingerprint recognition. Conventional minutiae extractors rely on a series of preprocessing steps, thus performing poorly for bad quality samples due to error accumulations. Existing extractors using convolutional neural networks are trained and tested with a certain specific sensor, thus requiring various modules for different sensors. To solve these problems, a universal minutiae extractor using a modified U-shaped segmentation network is proposed. Specifically, the proposed extractor classifies each pixel of a fingerprint image into a category of minutia with a certain orientation or non-minutia point, thus obtaining location and orientation information of minutiae simultaneously. The experimental results plus comparisons with other academic and commercial extractors prove that the proposed network can extract accurate and robust minutiae regardless of the quality of fingerprints and the sensor types.

中文翻译：

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使用卷积神经网络的通用指纹细节提取器

细节部分是指纹图像中广泛使用的特征点，直接决定了指纹识别的性能。常规的细节提取器依赖于一系列预处理步骤，因此由于错误累积而导致劣质样本的性能不佳。使用卷积神经网络的现有提取器通过特定的传感器进行训练和测试，因此需要用于不同传感器的各种模块。为了解决这些问题，提出了一种使用改进的U形分割网络的通用细节提取器。具体地，提出的提取器将指纹图像的每个像素分类为具有特定取向或非细节点的细节的类别，从而同时获得细节的位置和取向信息。