Aggregating minutia-centred deep convolutional features for fingerprint indexing

Abstract

Most current fingerprint indexing systems are based on minutiae-only local structures and index local features directly. For minutiae local structure, missing and spurious neighboring minutiae significantly degrade the retrieval accuracy. To overcome this issue, we employs deep convolutional neural network to learn a minutia descriptor representing the local ridge structures. Instead of indexing local features, we aggregate various number of learned Minutia-centred Deep Convolutional (MDC) features of one fingerprint into a fixed-length feature vector to improve retrieval efficiency. In this paper, a novel aggregating method is proposed, which employs 1-D convolutional neural network to learn a discriminative and compact representation of fingerprint. In order to understand the MDC feature, a steerable fingerprint generation method is proposed to verify that it describes the attributes of minutiae and ridges. Comprehensive experimental results on five benchmark databases show that the proposed method achieves better performance on accuracy and efficiency than other prominent approaches.