【摘要】 对基于滑动窗口进行样本扩充的单样本人脸识别方法进行了改进,改进后算法一方面在识别阶段采用了比原算法更少的特征,提高了识别的时间效率;另一方面在训练阶段获得原始样本的镜像样本作为附加的训练、注册集合,通过学习训练形成双子空间,识别结果由双子空间通过决策融合得到,提高了对测试样本变化的鲁棒性。在ORL人脸库和Feret子集人脸库上的实验表明,该算法在识别率上优于同类算法。更多还原

【Abstract】 To apply supervised learning method in single face recognition problem,an improved algorithm based on sample augments by sliding window is proposed.The recognition time of the proposed algorithm is shorter than that of the original algorithm because of less feature dimension.Moreover,the mirror samples are generated to constitute auxiliary training set and two subspaces can be obtained by subspace learning.The recognition result is from the decision fusion of two subspaces and is robust to variation of the test samples.The experiment results on ORL face database and subset of Feret face database show that the proposed algorithm has higher recognition accuracy than other similar algorithms.更多还原