Is Face Recognition best security?

a. pre-processing faces
We identify distinct face features such as the eyes, nose etc. A warp is performed by triangulating the image based On these features, and mapping the vertices of the triangle to the standard shape. The color of each pixel at given barycentric coordinates remains the same after this warp (see figure 4).We aim to find 20 distinct points on the face. These include the corners of the eyes, tip of the nose, nostrils, corners of the mouth etc. In profile faces where these features are occluded only the visible features are sought.
b. face recognition across pose
In this section we discuss the hard problem of how to establish reliable face recognition performance in the presence of severe pose changes. Conventional face recognition algorithms perform very unreliably when the pose of the probe face is different from the stored face because typical feature vectors vary more with pose than with identity. The emphasis in previous approaches to this problem has been on creating a model which can predict how a given face will appear when viewed at different poses. Prince and elder, presented a heuristic algorithm to construct a single feature which does not vary with pose.