P2CA: a new face recognition scheme combining 2D and 3D information

This paper presents a novel face recognition approach which uses only partial information in the recognition stage. The algorithm is based on an extension of the classical PCA and is called partial PCA (P2CA). The P²CA is a combined 2D-3D scheme which requires 3D face data in the training process but can process 2D pictures in the recognition stage. The strategy has been proven to be very robust in pose variation scenarios showing that the 3D training process retains all the spatial information of the face while the 2D picture effectively recovers the face information from the available data. Simulation results with a multi-view face database have shown recognition rates above 92% when using 180° texture face images in the training stage and 2D face pictures taken from different angles (from -90° to +90°) in the recognition stage.