本篇文章主要提出一種全自動三維人臉檢索方法。這裡將介紹一些新的方法來提高人臉檢索的強健性與效能。總共包含了: (1) 基於眼睛偵測的三維人臉角度校正; (2) 三維人臉的臉部特徵萃取(1.雙眼與鼻尖相對位置的夾角; 2.鼻子的體積; 3.鼻子的切片輪廓。)。本研究所提出的這三種特徵，可以代表每個人鼻子的形狀及大小，以及雙眼與鼻尖角度不同的特性。主要都是利用三維才有的深度以及曲率等特徵。最後，為了針對這些特徵進行檢索，將利用餘弦相似度去計算全部人臉模型的相似度。本實驗共用33名受測者，每名受測者建6個3D模型，而我們會利用查準率與查全率去探討這些特徵的性能表現。在我們設定取出相似度最高的前六筆資料進行檢索時，我們的查準率為70%，查全率也為70%。我們可以說本研究中所採用的三個特徵能夠有效的表示人臉特徵並進行檢索。

An automatic 3D face retrieval algorithm is presented. Several novelties are introduced to make the identification rate robust and efficient. These novelties include: (1) 3D face normalization based on eyes detection; (2) 3D facial features extraction (1.The angles between eyes and nose tip; 2.The nose volume; 3.Nose slices contour signature.). The approach works based on depth difference and curvature data. To retrieve the faces, we use the cosine similarity to calculate the similarity between all faces, and this system uses the two metrics (precision and recall) to determine the performance of the three features. In the experimental, the database is constituted by 33 subjects, and each subject has 6 3D face models. We can see that the precision rate is approaching around 70% while the top selection is set six; moreover, the recall rate is approaching around 70% in the same top selection. We can say that the three features are valid for feature representation and retrieval task.

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