基于QR分解重构虚拟样本的人脸识别算法

一直以来,小样本问题是人脸识别应用面临的一大难题。针对在实际人脸识别过程中存在的样本不足的问题,首次提出基于QR分解重构虚拟训练样本的算法。该算法使用Q与R的部分信息构造出与原始人脸图像具有一定差异性的虚拟样本,增加了人脸图像更多可能性变化的有效特征,扩大了训练样本集,然后对原始样本和虚拟重构样本协同表示的结果进行加权融合,选取最优权重组合,调整原始样本与虚拟样本对结果的影响比重,得到正确识别率。以ORL、FERET和AR三大人脸数据库对算法进行实验验证。实验结果表明,此算法能够取得较高的识别准确率。

A face recognition algorithm based on QRdecomposition and reconstruction of virtual samples

The small sample problem has been a major challenge for face recognition applications for a long time. Concerning the insufficient sample problem in the face recognition process, we propose a face recognition algorithm based on QR decomposition and reconstruction of virtual training samples. We use partial Q and R information to construct virtual samples which have certain difference from the original face image, thus more effective characteristics of the face image that likely change with emotions and illumination are increased, and the training sample sets are expanded. Then we perform weighting fusion on the collaborative representations of the original samples and virtual samples, select the optimal weight combination, adjust the proportions of the original sample and virtual sample which can impact on the results, and obtain the correct recognition rate. Experiments on the ORL, FERET and AR face data sets show that the proposed algorithm can achieve a higher recognition accuracy.