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標題: 即時多人臉辨識之影像資料庫系統
Real Time Multiple Face Recognition System on Image Database
作者: 林俊傑
Lin, Chun-Chieh
關鍵字: Face Detection;人臉偵測;Face Recognition;PCA;SURF;Adaboost;人臉辨識;主成分分析;快速強健特徵
出版社: 資訊網路多媒體研究所
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人臉偵測與辨識技術近年來已漸趨成熟,應用面更相對多元,其中以智慧型手機、門禁管制、即時監控、犯罪蒐證等安全性應用為主。本論文提出一個即時人臉偵測與辨識系統。在人臉偵測上,應用Adaboost方法的Cascade分類器,結合SURF (Speeded Up Robust Features) 的偵測物體方法來偵測人臉多視角區域和五官區域,並透過五官區域的位置縮小了人臉區域的範圍,藉此排除不必要的背景干擾。在人臉辨識上,分為訓練階段與測試階段。在訓練階段中,我們將同一類別影像所取得的人臉及五官區域使用PCA (Principal Component Analysis) 方法得到投影矩陣,透過此投影矩陣取得此類別的特徵向量。在測試階段,待測影像先進行人臉和五官區域的偵測並進行影像大小的正規化以及轉至YCbCr空間上進行亮度差異排除的前置處理,接著將前置處理後的影像,分別以各類別在訓練階段所取得之投影矩陣,透過PCA方法進行特徵向量擷取,並使用歐氏距離配合權重的方法將此影像的特徵向量與資料庫中的特徵向量進行特徵的比對,最後將此影像辨別為與其特徵向量差異最小的類別,即得到辨識結果。

The technique for face detection and recognition have been matured on these years, and it is applied to many different fields, including smart phone, the building entrance guard control, the real-time monitor system, and the criminal verification. This paper presents an automated system for human face detection and recognition. In the face detection, we use the cascade classifier based on Adaboost and SURF (Speeded Up Robust Features) methods to detect multi-view faces and facial regions, we also narrow the region of the face by the location of facial regions such that unnecessary background can be ruled out. In the face reorganization, there are two stages. One is the training stage and the other is the testing stage. In the training stage, we use PCA (Principal Component Analysis) to obtain a projection matrix and an eigenvector for each facial region of the same person. In the testing stage, facial regions of the testing image are detected first and preprocessing, including size normalization of facial regions and luminance removal in the color space of YCbCr, is then executed. Those preprocessed facial region images are projected with projection matrix obtained in the training stage to acquire their corresponding eigenvectors. We use Euclidean distance together with weight concept to evaluate the difference of the testing eigenvectors and those in the database. Finally, the testing image is classified into the class with least difference.
We use IMM and FEI face databases for the experiments of face detection, and we use ORL and UMIST face databases for the experiments of face recognition. The results show that our face detection rates are 97.92% in IMM face database and 93.83% in FEI face database. In addition, our face recognition rates are 81.75% in ORL face database and 88.25% in UMIST face database. The performance of the proposed method is more robust than that of other methods against the expression and multi-view variations.
其他識別: U0005-2305201222443600
Appears in Collections:資訊網路與多媒體研究所

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