Dynamic Pore Filtering for Keypoint Detection applied to Newborn Authentication

We present a novel method for newborn authentication that matches keypoints in different interdigital regions from palmprints or footprints. Then, the method hierarchically combines the scores for authentication. We also present a novel pore detector for keypoint extraction, named Dynamic Pore Filtering (DPF), that does not rely on expensive processing techniques and adapts itself to different sizes and shapes of pores. We evaluated our pore detector using four different datasets. The obtained results of the DPF when using newborn dermatoglyphic patterns (2400ppi) are comparable to the state-of-the-art results for adult fingerprint images with 1200ppi. For authentication, we used four datasets acquired by two different sensors, achieving true acceptance rates of 91.53% and 93.72% for palmprints and footprints, respectively, with a false acceptance rate of 0%. We also compared our results to our previous approach on newborn identification, and we considerably outperformed its results, increasing the true acceptance rate from 71% to 98%.