Face Recognition using a Fast Model Synthesis from a Profile and a Frontal View

In our previous work we presented a new 2D-3D mixed face recognition scheme called Partial Principal Component Analysis (P²CA). The main contribution of P²CA is that it uses 3D data in the training stage but it accepts either 2D or 3D information in the recognition stage. We think that 2D-3D mixed approaches are the next step in face recognition research since most of surveillance or access control applications only dispose of a single camera which is used to acquire a single 2D texture image. Nevertheless, one of the main problems of our previous work was the enrollment of new persons in the database (gallery set) since a total of five different pictures are needed for getting the 180deg texture maps (manual morphing). Thus, this work is focused on the automatic and fast creation of those 180deg texture maps from only two images (frontal and profile views). Preliminary results show that there is not a significant degradation of the recognition accuracy when using this automatically and synthetically created gallery set instead of the one created by morphing the five views manually.