SIFT/LBP 3D face recognition

3D face recognition is a promising alternative to face the problem of recognizing 2D robustness. Therefore, the main advantage of 3D face recognition-based approach uses all the information on the geometry of the face, which allows us to get an accurate representation of the face. In the proposing contribution all distinctive facial features are captured by first extracting SIFT (Scale Invariant Feature Transform) key points, then we applied the operator SIFT on LBPP,R (Local Binary Pattern) images, separately. Following the work of Faltemier and al. [7] then Tang and al. [22] we can better detect a number of key points by using SIFT on LBPP, R images, that using SIFT on the original images of the face analysis then measuring how the face changes along profiles, built between pairs of key points. The contribution is tested using whole of the Face Recognition Grand Challenge FRGC v1.0 data. Finally, we perform a classification based on SVM process. Keywords—3D biometric model, Biometrics, Face recognition.