Universal fingerprint minutiae extractor using convolutional neural networks

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.