Phase-PCANet for Fingerprint Image Liveness Detection

This paper proposes Phase-PCANet, a new image representation method for fingerprint liveness detection. Phase-PCANet integrates both local and global phase features. The local phase, capturing fine-grained edges and textures, is extracted via short-time Fourier transform with singular value decomposition; the global phase, encoding the holistic structural layout, is obtained from a full-image Fourier transform. These two phase components are separately fed into an improved PCANet, which employs dual binary coding, i.e., scalar-based intra-channel coding and vector-similarity-based inter-channel coding, to preserve within-channel structures and cross-channel correlations. Multi-stage features from different PCA layers within each phase path are aggregated, and the resulting deep features from both paths are concatenated to form the final image representation. Experiments on the LivDet 2011, 2013, and 2015 databases verify the effectiveness of the proposed method.