本論文主要在探討在監視影片中，透過Gabor小波轉換取得影中人之步態特徵點，進而再透過使用分數形式的指數項多項式的核主成分分析來針對特徵點分類。因為人體步態的特徵點擷取大致上可分為時間及空間兩個層面，而本論文為了不失去影片中任何的資訊，因而採取時空兩者之資訊即影中人步行之剪影(silhouette)後,再針對剪影部分，以Gabor小波為基礎的迴積而取得該步態之特徵點，接著使用指數項為分數形式的多項式為核主成分分析來使前述的特徵點來進行分類的動作後，接著再以馬氏距離(Mahalanobis distance)來計算其相似度。最後進行模擬與實驗結果，可以展現以Gabor小波轉換為基礎之特徵點的確可以表現出較佳之辨識度。

In this thesis, we propose a method to extract the human gait features from the surveillance video through Gabor wavelet transformation, and then we classify these features by kernel principle component analysis (PCA) with the fractional power polynomial model. Because human gait feature extraction can be categorized into spatial and temporal domain, we will discuss the gait features in these two domains. In order not to lose any information from the surveillance video, this thesis uses the spatial-temporal silhouette of the people walking in the surveillance video, then we can have the gait features by taking silhouette convolution with Gabor based wavelet transformation. We classify these features by kernel PCA with the fractional power polynomial model. Finally, we use Mahalanobis distance to measure the similarity between the gait features. The simulation and the experiment results show that Gabor-based kernel PCA with fractional power polynomial models for Gait recognition have a better performance.

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