臉部辨識-支撐向量機法

人臉的偵測和辨識在錄影監視、個人安全及人臉影像資料庫管理中扮演重要的角色。本論文中臉部辨識的核心採用支撐向量機法 (SVM)。支撐向量機法不需像幾何關係法和型態法設定許多關係條件，即可進行臉部辨識。支撐向量機法在處理分類問題時，不需建立知識資料庫 (如模糊理論中的規則資料庫) 可將輸入資料有效分類，並獲得支撐向量 (SV) 和邊界 (Margin)等資訊。拉格朗日支撐向量機 (LSVM) 使用迭代法來提升計算速度。我們將眼睛和嘴有效的轉換成支撐向量機計算的格式，並分別使用拉格朗日支撐向量機計算眼睛和嘴的邊界做為辨識的依據。在本系統中，採用包含92張照片和31個不同的人CVL臉部影像資料庫做系統實驗。

關鍵字

臉部辨識；支撐向量機；臉部偵測

並列摘要

Human face detection and recognition plays an important role in application such as video surveillance, personal security and face database management. A novel Support Vector Machines (SVM) is adopted for face recognition. SVM can handle classification problem effectively without establishing the prior knowledge database, and obtain support vector and related margin. To shorten the computing time, a modified version of SVM, namely Lagrangian support vector machine (LSVM) is applied here. An effective method to deal with the eyes and mouth region is proposed in this thesis. We verify the correction rate of the utilize method via a database, CVL, that contains 91 images of 31 individuals.

並列關鍵字

Face recognition ； Support vector Machine ； Face detection

參考文獻

machines,” Journal of Machine Learning Research, vol. 1, pp. 161–

via morphology-based pre-processing,” Pattern Recognition,

[5] K. Sobpttka and I. Pitas, “Face locakization and feature extraction

Conference Image Processing, pp. 483–486, 1996.

[7] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of

國際替代計量

臉部辨識-支撐向量機法

全文下載

主題瀏覽