DEEPPOREID: An Effective Pore Representation Descriptor in Direct Pore Matching
Yuanhao Zhao, Guojie Liu, Feng Liu, Linlin Shen, Qin Li · 2019
This paper proposes an effective pore representation descriptor based on Convolutional Neural Networks (CNNs). We make full use of the diversity and large quantities of sweat pores in fingerprints to learn a deep feature, denoted as DeepPoreID. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. Experiments carried on the challenge public high-resolution fingerprint database with small image size of 320 × 240 shows the effectiveness of the proposed DeepPoreID. The results also have shown that the proposed method outperforms other existing state-of-the-art methods in the aspect of recognition accuracy. About ~35% rise in accuracy can be obtained when compared with the best result achieved by existing methods.