臉部特徵定位結合主動型狀模型及支持向量機

Abstract

主動形狀模型近幾年在電腦視覺方面被充分的注意，在實際應用層面也涵蓋了許多不同方向諸如醫學影像、臉部特徵定位等。 此模型的特色為它同時結合了形狀及表面顏色這兩種資訊，透過一些訓練後即可用來處理一般問題且達到不錯的成果。但因為 此模型中一些為了逼近而作的假設，使得有些時候搜尋特徵點時容易進入區域的最小值而導致不好的結果。 另一方面，支持向量機是近幾年在機器學習及圖型辨識中人們常用的工具。它的優點為訓練過程及判斷過程皆很迅速，但最後的成果往往 與訓練時使用的樣本有很大的關係。 在這篇論文中，我們在臉部特徵定位上加上了與前人研究截然不同的觀點。我們結合了主動形狀模型及支持向量機並透過巧妙的運用， 支持向量機可在臉部特徵定位過程中提供額外寶貴的資訊，而這些資訊將使主動型狀模型遠離掉入區域極小值的機會並使最後臉部特徵定位的 結果更完善。

Active Shape Model(ASM) causes great attention in recent years, it can be used in many different application including medical imaging, face alignment etc. One of the characteristic of ASM is combining shape and intensity, with carefully training it can locate features in given image . But due to some assumption and approxiamation, sometimes the search falls into local minima. On the other hand, Support Vector Machine is a tool that many researcher use in machine learning or pattern recognition area. Its advantage is the speed of training and classification although the performance is highly relative to training samples. In this paper, we propose a novel view in face alignment. With combining ASM and SVM, we obtain additional information instead of local intensity. These information help the search away from local minima and improve the performance of alignment.