三維人臉辨識自動特徵擷取系統之研究

The Study of Automatic Feature Capture System for 3D Face Recognition

黃彥傑(Yan Jie Huang)

指導教授 ： 吳明川

國立臺北科技大學/機電學院/自動化科技研究所/碩士(2006年)

摘要

本研究使用雙CCD攝影機作為取像設備，發展一套自動化特徵擷取之三維人臉辨識系統，在特徵點的擷取先將五官分別分割出來，再使用本研究所提出動態膚色閥值法簡稱(DSCS)，及亮度邊緣法，具有較不易受光源所影響之優點，可以有效取得特徵點，減少比對錯誤。再利用相關仿射係數不變之原理來做為辨識依據，達到資料量少的效果。在參考平面特徵點選取為眼睛，減少人臉轉動時特徵點消失，造成無法比對，在人臉轉動角度不大時，系統仍能正確辨識。根據PCA樣本訓練的概念，增加多張附人臉變化之樣本訓練，對系統之人臉辨識率有顯著的提升。

關鍵字

立體視覺 ； 人臉辨識 ； 相關仿射結構

並列摘要

This research develops a system which can recognize three-dimensional human facial expression. In this study, Dynamic Skin Color Scale (DSCS) and Grey Edge Method are used to capture the key feature points. DSCS techniques have the advantage of less effect by light source. In order to effectively obtain the feature point and minimize the error comparison, the recognition process is divided into two steps and combined with RGB contrast enhancement. In addition, some affine coefficient invariable theorem are used as the basis for recognition. The selection of system reference plan is at the eye position, therefore, the feature point will not missing when the face is moving. In order to enhance the robust recognition, many types of expression training are added to each Relative Affine coefficient.

並列關鍵字

Stereo Vision ； Face Recognition ； Relative Affine Structure

參考文獻

[1] K. Fukunaga, “Introduction to Statistical Pattern Recognition”, Academic Press, second edition, 1991.

[2] Chih-Pin Liao, Jen-Tzung Chien, “Nonsingular Discriminant Feature Extraction for Face Recognition”, IEEE International Conference on Acoustics Speech and Signal Processing, vol.2, pp.957-960,March.2005.

[3] Jian Yang, and Jing-yu Yang, “Why Can LDA be Performed in PCA Transformed Space?”, Pattern Recognition, Vol.36, pp.563-566, Feb 2003.

[4] 洪倩玉，建立動態鑑別式分析於線上人臉辨識與驗證，碩士論文，國立成功大學，資訊工程研究所，2003。

”Two-Dimensional PCA: A New Approach to Representation and Recognition”, IEEE Transactions on pattern analysis and machine intelligence, Vol.26, No.1, pp.131-137, Jan. 2004.