一般三維人臉重建取樣的樣本，往往需要使用設備昂貴的雷射掃描器，而本論文採用Microsoft KINCET感應器。它與昂貴的雷射掃描器相較下是屬於成本較為低廉的，KINECT具有深度資訊及彩色影像資訊，實驗環境以180°多視角掃描真實人臉三維表面影像，利用ICP演算法(Iteration Closest Point, ICP) 進行多視角人臉匹配，重建三維人臉模型。人臉辨識採用3D SIFT (3D Scale Invariant Feature Transform) 演算法提取人臉特徵關鍵點，並使用歐式距離計算三維空間座標特徵點與特徵點距離的權重關係。本論文提出的辨識方法在GavabDB公用資料庫辨識率可以達到83.6%。 In the past years, most of the three-dimensional reconstruction or recognition systems use two-dimensional image and its depth image to calculate the three-dimensional coordinates of the image to process the three-dimensional theme. Such operations usually take a considerable amount of computing costs. This research proposes another approach, point cloud, which can preserve feature vectors and color information, for three-dimensional face reconstruction and recognition. In the conventional 2D approach, it keeps tracking the information of each pixel of the 2D image. On the other hand, the point cloud system directly synthesizes the 2D image and its depth image into a point cloud model with 3D coordinates. Therefore it can reduce the computation complexity significantly. It can further construct a 3D space coordinate KD-Tree query system to accelerate the query search speed for searching the key points of the 3D coordinate.
Generally, it uses some expensive equipments and laser scanners for three-dimensional facial reconstruction. In this research we try to use the Microsoft KINCET sensor to reconstruct the 3D human face. Compared with the expensive laser scanner, KINECT has the characteristics of cheap cost and can find the information of color image and depth image. In this research KINET is used to scan the human face in multi-view within 180. Then we use the iteration closest point (ICP) algorithm to match the multi-view human faces. By this approach the 3D data base group points of the human face can thus be established. The three-dimensional face model point cloud data via 3D SIFT (3D Scale Invariant Feature Transform) algorithm is applied to extract the feature key points. Then we use the three-dimensional coordinates of Euclidean distance to calculate the feature points and feature weights distance relationship to determine whether the face belongs to the same person. The experimental results show that under Gavab DB face database our approach has the recognition rate of 83.6%.