支撐向量機應用於嘴唇影像辨識基於活動基模型

A SVM Lips Recognition Method Based on Active Basis Model

陳勇志(Yung-chih Chen)

指導教授 ： 許志宇

朝陽科技大學/資訊學院/資訊與通訊系碩士班/碩士(2010年)

摘要

本論文提出了一種新穎的嘴唇辨識方法利用活動基模形(Active Basis Model, ABM)。此方法的流程架構主要包含四個階段。在第一階段，取得嘴唇圖形的變形樣板。這些製作變形樣板的嘴唇圖形為明顯開嘴或明顯閉嘴。第二階段，取得測試圖形的基礎樣板。第三階段，比較訓練出的變形樣板與每張圖片的基礎樣間的概似性，並作為特徵向量。最後，利用支撐向量機（Support Vector Machine，SVM）分類第三階段的向量特徵。在本文中使用支撐向量機分類嘴唇是一種監督式的學習。本文從BioID與PCIS人臉資料庫中擷取出嘴唇圖片作為實驗資料庫，準確率最高可達96.375％。實驗結果證明本文提出的方法對於嘴唇影像識別擁有極佳的辨識結果。嘴唇的辨識结果可用於自動語音識別與臉部辨識系統。

關鍵字

活動基模型 ； 嘴唇識別 ； 賈巴小波 ； 支撐向量機

並列摘要

The paper proposed a novel method for lip recognition based on Active Basis Model (ABM). There are four stages in a flowchart of this novel method. At the first stage, the deformable templates of lip images is obtained. The lip images of deformable templates are obvious open or closed. The second stage is to obtain the deformed template of each testing images. The third stage, the difference between the deformable template and deformed template is calculated and used as a feature vector. Finally, the support vector machine (SVM) is use to classify the feature vector. SVM is a supervised learning to be a classifier for lip recognition. Experimental results are used to verify the proposed method has good performance. There are 1000 face images in BioID and PCIS face database used for the experiment. The most accuracy of results is 96.375%. Lips classification results can be used in automatic speech recognition and face recognition system.

並列關鍵字

Active Basis Model ； Support Vector Machine ； Lips Recognition ； Gabor wavelet

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