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Feature fusion with covariance matrix regularization in face recognition

机译：人脸识别中的协方差矩阵正则化特征融合

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AbstractThe fusion of multiple features is important for achieving state-of-the-art face recognition results. This has been proven in both traditional and deep learning approaches. Existing feature fusion methods either reduce the dimensionality of each feature first and then concatenate all low-dimensional feature vectors, named as DR-Cat, or the vice versa, named as Cat-DR. However, DR-Cat ignores the correlation information between different features which is useful for classification. In Cat-DR, on the other hand, the correlation information estimated from the training data may not be reliable especially when the number of training samples is limited. We propose a covariance matrix regularization (CMR) technique to solve problems of DR-Cat and Cat-DR. It works by assigning weights to cross-feature covariances in the covariance matrix of training data. Thus the feature correlation estimated from training data is regularized before being used to train the feature fusion model. The proposed CMR is applied to 4 feature fusion schemes: fusion of pixel values from 3 color channels, fusion of LBP features from 3 color channels, fusion of pixel values and LBP features from a single color channel, and fusion of CNN features extracted by 2 deep models. Extensive experiments of face recognition and verification are conducted on databases including MultiPIE, Georgia Tech, AR and LFW. Results show that the proposed CMR technique significantly and consistently outperforms the best single feature, DR-Cat and Cat-DR.
机译： 摘要 多种功能的融合对于实现最新的人脸识别结果非常重要。传统和深度学习方法都证明了这一点。现有的特征融合方法要么先降低每个特征的维数，然后再将所有低维特征向量（称为DR-Cat）连接起来，反之亦然，将其称为Cat-DR。但是，DR-Cat忽略了不同特征之间的相关信息，这对于分类很有用。另一方面，在Cat-DR中，从训练数据估计的相关信息可能不可靠，尤其是在训练样本的数量有限的情况下。我们提出了协方差矩阵正则化（CMR）技术来解决DR-Cat和Cat-DR的问题。它通过为训练数据的协方差矩阵中的跨功能协方差分配权重来工作。因此，在用于训练特征融合模型之前，将从训练数据估计的特征相关性进行正则化。提出的CMR应用于4种特征融合方案：3个颜色通道的像素值融合，3个颜色通道的LBP特征融合，单个颜色通道的像素值和LBP特征融合，2提取的CNN特征融合深层模型。在MultiPIE，Georgia Tech，AR和LFW等数据库上进行了面部识别和验证的广泛实验。结果表明，提出的CMR技术显着且始终优于最佳的单一功能DR-Cat和Cat-DR。

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