基於KINECT建立3D人臉模型用於人臉辨識系統

Building 3D face model used to face recognition base on Kinect

許維新(Wei-Hsin Hsu)

指導教授 ： 謝景棠

淡江大學/工學院/電機工程學系碩士班/碩士(2014年)

https://doi.org/10.6846/TKU.2014.00127

摘要

在人臉辨識的相關研究中，側臉的辨識率因側臉的特徵資訊相較正臉來的少，造成辨識上的困難，使得辨識率一直研究學者想要大幅突破的目標，透過增加訓練資料的數量，有助於辨識率的提升，為了蒐集各種角度下之人臉影像，往往耗費大量時間。本論文提出一僅藉由單張正面影像模擬出各角度人臉之3D人臉建模方法，達到節省蒐集各種角度人臉之時間。透過Kinect擷取單張正面彩色及深度影像，經過前處理濾除非人臉之區塊並正規化後，在OpenGL空間中顯示3D人臉模型，透過OpenGL視窗視野角度改變，可以模擬出各種角度下之人臉，將這些人臉保存成2D影像，接著使用PCA降低維度後送入SVM分類。在實驗結果中顯示，用於人臉辨識有不錯之結果。

關鍵字

人臉辨識 ； PCA ； SVM ； Kinect

並列摘要

The applications of face recognition is increasing day by day. The studies of face recognition are mainly occlusions (sunglasses, hats, masks) , illumination (shadows, glare) , facial expressions, age variation , and pose changes (profile, pitch angle is too large) etc, and the side face recognition has been a difficult problem. We propose a simpler and faster method to create 3D face model. First, using ASM(Active Shape Model) to detection and get the color and depth image of face by Kinect, then we based on the information of the depth image portrayed face in opengl three-dimensional. This method retains much texture of information of the original face images, and to create a complete change of face uneven depth. It still has a good result of repairing the distortion in side face. We can get a set face images of the same person with different angles by the method proposed in this paper. In recognition part, we use PCA(Principal Component Analysis) to reduce the dimensions, and combined SVM(Support Vector Machine) to classify. Experiments show that the side face recognition can have good results.

並列關鍵字

Face Recognition ； ASM ； PCA ； SVM ； Kinect

參考文獻

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