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首页> 外文会议>International Conference on Neural Information Processing(ICONIP 2004); 20041122-25; Calcutta(IN) >Face Recognition Using Weighted Modular Principle Component Analysis
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Face Recognition Using Weighted Modular Principle Component Analysis

机译：加权模块化主成分分析的人脸识别

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摘要

A method of face recognition using a weighted modular principle component analysis (WMPCA) is presented in this paper. The proposed methodology has a better recognition rate, when compared with conventional PCA, for faces with large variations in expression and illumination. The face is divided into horizontal sub-regions such as forehead, eyes, nose and mouth. Then each of them are separately analyzed using PCA. The final decision is taken based on a weighted sum of errors obtained from each sub-region. A method is proposed, to calculate these weights, which is based on the assumption that different regions in a face vary at different rates with expression, pose and illumination.
机译：本文提出了一种使用加权模块化主成分分析（WMPCA）的人脸识别方法。与传统PCA相比，该方法对于表情和照度变化较大的面部具有更好的识别率。面部分为水平的子区域，例如前额，眼睛，鼻子和嘴巴。然后使用PCA分别分析它们中的每一个。基于从每个子区域获得的误差的加权总和来做出最终决定。提出了一种用于计算这些权重的方法，该方法基于以下假设：面部的不同区域随表情，姿势和照明以不同的速率变化。

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