MinNet: Minutia Patch Embedding Network for Automated Latent Fingerprint Recognition
In this study, we proposed a novel minutia patch embedding network (MinNet) model for latent fingerprint recognition problem. Embedding vectors generated for a fixed-size patch extracted around a minutia are used in the local similarity assignment algorithm to produce a global similarity match score. Unlike earlier minutia embedding models that aim to discriminate between latent image and sensor image minutia pair embeddings using L2 distance between the embedding vectors in the training process, MinNet model jointly optimizes the spatial and angular distribution of neighboring minutiae and ridge flows of the patches. Even though the proposed model is trained using weakly labeled training data, it produces state-of-the-art results thanks to it ability to generate discriminative embeddings. Proposed method has been evaluated on several public and private datasets and compared to popular latent fingerprint recognition methods presented in earlier studies. Our proposed method significantly outperforms existing methods on all three databases utilized in our study.