應用核心主成份分析及非平衡式SVM於影像中人臉偵測之研究

Face Detection Using Kernel PCA and Imbalanced SVM

陳衍廷(Yen-Ting Chen)

指導教授 ： 劉益宏

中原大學/工學院/機械工程學系/碩士(2006年)

https://doi.org/10.6840/cycu200600267

摘要

摘要
自動化人臉辨識、表情辨識系統等人身安全相關的新興科技在近代中受到愈來愈多的重視，也吸引了許多研究學者從事相關研究。然而在人臉辨識系統中，第一步驟是要找出影像中的人臉，且人臉辨識成功率也極度依賴於人臉偵測的正確性，因此人臉偵測系統在此類系統中扮演重要的角色。本論文致力於開發多角度人臉偵測與追蹤之系統，系統可分為靜態與動態影像人臉偵測模式。為了解決非平衡資料集合問題(Imbalanced dataset)，我們使用非平衡式支持向量機器(Imbalanced Support Vector Machine, ISVM)並結合核心主要成份分析演算法(Kernel Principal Component Analysis, KPCA)建構人臉與非臉分類器，並以中原大學多角度人臉影像資料庫及非臉影像組成訓練資料，從分類器建構的結果中可以得知，使用KPCA所抽取之特徵訓練所得之ISVM分類器具有最佳人臉偵測效果。在靜態影像人臉偵測系統架構中，我們運用膚色區塊分割以減少搜尋區域；在動態影像人臉偵測系統架構中，則運用移動物體及其膚色偵測以減少搜尋區域，並結合CAMSHIFT演算法以追蹤影像中之人臉，從實驗結果可以看出使用ISVM分類器可以大量地減少人臉被判定非臉的影像，本論文所提出之多角度人臉偵測系統可從影像當中偵測出不同角度、大小、表情之人臉，並加以追蹤。
關鍵字：人臉辨識、人臉偵測、支持向量機器、核心主要成份分析、非平衡資料集合

關鍵字

人臉偵測 ； 人臉辨識 ； 支持向量機器 ； 核心主要成份分析 ； 非平衡資料集合

並列摘要

Abstract
Fully automatic face and expression recognition systems have received more and more attention in recent years. However, in such kind of systems, the first step is to detect faces from images and the recognition rate extremely relies on the accuracy of face detection process, therefore, face detection system plays a crucial role in a face recognition system. This thesis aims to develop a multi-view face detection system. Our system is composed of two sub-systems：(1) one is for detecting faces from still images, (2) and the other is for detecting faces from image sequences. In order to solve the problem of imbalanced dataset, we apply imbalanced SVM (ISVM) and KPCA to data sets in order to extract image features. The experimental results show that combing the KPCA feature extractor and the ISVM classifier can yield higher face-detecting rate compared to SVM. Before detection in still image mode, the method of skin-region segmentation is utilized to find the possible regions, while both moving objects tracking technique and skin-region segmentation method are applied to eliminate the redundant searching regions in dynamic sequences mode. The system also aggregates with CAMSHIFT algorithm for fast face tracking. The experimental results show that the proposed multi-view face detection system can detect and track faces varying in orientations, sizes, and expressions in images efficiently.

Keywords: Face recognition, face detection, support vector machines, kernel principal components analysis (KPCA), imbalanced dataset

並列關鍵字

imbalanced dataset ； Face recognition ； face detection ； support vector machines ； kernel principal component analysis (KPCA)

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