Cognitive Information Processing in Face Recognition

Gorn Tepvorachai, Chris Papachristou, Frank Wolff, Robert Ewing
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引用次数: 3

Abstract

In the conventional eigen face method, the principle component analysis (PCA) algorithm associates the eigen vectors with the changes in illumination. In this paper, we propose an improvement of facial image association for face recognition using a cognitive processing model. This method is based on the notion of multiple-phase associative memory. The Essex face database is used to verify our model for facial image recognition and compare the results of face recognition with conventional eigen face method. The simulation results show that the proposed cognitive processing model approach results in better performance than that of the conventional eigen face approach; while the computational complexity remains of the same magnitude as that of the eigen face method.
人脸识别中的认知信息处理
在传统的特征人脸方法中,主成分分析(PCA)算法将特征向量与光照变化相关联。本文提出了一种基于认知处理模型的人脸图像关联识别方法。该方法基于多阶段联想记忆的概念。利用Essex人脸数据库验证了我们的人脸图像识别模型,并与传统的特征人脸识别方法进行了比较。仿真结果表明,所提出的认知加工模型方法比传统的特征人脸方法具有更好的性能;而计算复杂度与特征面法保持在同一量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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