Since the existing 3D face acquisition technology has many restrictions on gathering scene, a 3D face reconstruction technology based on several images was proposed, and its validation was verified. First, an iterative computing model of pose and depth value estimation was proposed to implement the accurate estimation of feature depth. Then the depth values integration based on several images and shape modeling were further investigated. Finally, the Iterative Pose and Depth Optimization (IPDO) algorithm was compared with Nonlinear Least-Squares Model with Symmetry and Regularization terms (NLS1_SR) on Bosphorus database, the modeling precision was improved by 9%, and the projected image of 3D model is similar to the 2D inputted image. The experimental results show that under the condition of big pose change, the proposed recognition algorithm assisted by 3D information can improve the recognition rate of more than 50%.

With serious sidelight, it is difficult for the traditional algorithm to eliminate shadows. To improve the illumination compensation effect, a logarithmic transformation function was presented. In order to improve the performance of face recognition, by taking this problem as a classic pattern classification problem, a new method combining Local Binary Pattern (LBP) and Support Vector Machine (SVM) was proposed. One-against-one was used to convert multi-class problem to two-class problem, that can be used by SVM. Simulation experiments were conducted on the database of CMU PIE, AR, CAS-PEAL and one face database collected by the authors. The results show that lighting effects can be well eliminated and the proposed method performs better than the traditional ones.