Subspace modeling plays an important role in face recognition. Independent Component Analysis (ICA), a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Component Analy...Subspace modeling plays an important role in face recognition. Independent Component Analysis (ICA), a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Component Analysis (PCA) technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis (DICA) is proposed, which combines the strength of unsupervised learning of ICA and supervised learning of Linear Discriminant Analysis (LDA), and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called 'discriminant independent faces' are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA+LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.展开更多
针对有噪的ICA模型,提出一种有限制的平均场近似(restrictive m ean field approxim ation,RMFA)的算法来求解ICA模型参数和源信号的估计问题。在传统MFA-ICA算法的基础上,提出将ICA中的模型参数和源信号均限制为非负,目的是使得提取出...针对有噪的ICA模型,提出一种有限制的平均场近似(restrictive m ean field approxim ation,RMFA)的算法来求解ICA模型参数和源信号的估计问题。在传统MFA-ICA算法的基础上,提出将ICA中的模型参数和源信号均限制为非负,目的是使得提取出的特征更独立,更利于识别。通过手写体数字和仿真模拟人脸图形以及ORL人脸数据进行实验,将RMFA-ICA算法与传统的ICA算法和无限制的MFA-ICA算法进行比较,对于手写体数字和仿真模拟人脸图形,RMFA-ICA算法能分离出更独立的特征,对于ORL人脸数据,其结果表明,利用RMFA-ICA算法明显优于传统ICA算法和无限制MFA-ICA算法识别结果。展开更多
基金Supported by the Key Project of the National Natural Science Foundation of China(No.90104030)the National Natural Science Foundation of China(No.60401015)
文摘Subspace modeling plays an important role in face recognition. Independent Component Analysis (ICA), a multivariable statistical analysis technique, can be seen as an extension of traditional Principal Component Analysis (PCA) technique, which addresses high order statistics as well as second order statistics. In this paper, a new scheme of subspace-based representation called Discriminant Independent Component Analysis (DICA) is proposed, which combines the strength of unsupervised learning of ICA and supervised learning of Linear Discriminant Analysis (LDA), and efficiently enhances the generalization ability of ICA-based representation method. Based on DICA subspace analysis, a set of optimal vectors called 'discriminant independent faces' are learned from face samples. The effectiveness of our method is demonstrated by performance comparisons with some popular methods such as ICA, PCA, and PCA+LDA. On the large scale database of IIS, significant improvements are observed when there are fewer training samples per person available.
文摘针对有噪的ICA模型,提出一种有限制的平均场近似(restrictive m ean field approxim ation,RMFA)的算法来求解ICA模型参数和源信号的估计问题。在传统MFA-ICA算法的基础上,提出将ICA中的模型参数和源信号均限制为非负,目的是使得提取出的特征更独立,更利于识别。通过手写体数字和仿真模拟人脸图形以及ORL人脸数据进行实验,将RMFA-ICA算法与传统的ICA算法和无限制的MFA-ICA算法进行比较,对于手写体数字和仿真模拟人脸图形,RMFA-ICA算法能分离出更独立的特征,对于ORL人脸数据,其结果表明,利用RMFA-ICA算法明显优于传统ICA算法和无限制MFA-ICA算法识别结果。
