Deeply Learned Pore-Scale Facial Features

Pore-scale facial features consist of pores, fine wrinkles, and hair, which commonly appear in the whole face region. Similar to iris features and fingerprint features, pore-scale facial features are one of the biometric features that can distinguish human identities. Most of the local features of biometric depend on hand-crafted design. However, such hand-crafted features rely heavily on human experience and are usually composed of complicated operations, costing a great deal of time. This paper introduces a novel pore-scale facial features - Deeply Learned Pore-scale Facial Features (DLPFF). We use Convolutional Neural Networks (CNNs) to learn discriminant representations of pore-scale facial features. Experiments show that our deep network based method outperforms the state-of-the-art methods on the Bosphorus database.