Detecting Locally, Patching Globally: An End-to-End Framework for High Speed and Accurate Detection of Fingerprint Minutiae

Billions of fingerprint images are acquired and matched to protect the national borders and in a range of egovernance applications. Fast and accurate minutiae detection from fingerprint images is the key to advance fingerprint matching algorithms for large-scale applications. However, currently available fingerprint minutiae extraction methods are not accurate and fast enough to support such large-scale applications. This paper proposes a new method that uses a lightweight pixelwise local dilated neural network to extract local features and a patch-wise global neural network to recover the global features. It consolidates the local and global fingerprint features to generate a full-size minutiae location map, and then accurately localizes the minutiae positions by using a recursive connected components algorithm. We design a new loss function to accurately detect minutia orientation and incorporate a dynamic end-to-end loss to provide effective supervision in learning discriminant features. It is due to the proposed design and loss function that can enable higher accuracy with significantly less computations. We present reproducible experimental results from five publicly available contact-based and contactless databases that indicate significant improvement in the minutiae detection accuracy, which also leads to enhanced fingerprint matching accuracy. Since the minutiae represent key points in the fingerprint images, the proposed end-to-end minutiae detection method also has a potential to be employed in many other key points detection tasks.