深层融合对称子空间学习稀疏特征提取模型

摘要

提出深层融合对称子空间学习稀疏特征提取模型.在深度子空间基础上,引入对称性、稀疏性约束,通过构建深层映射网络,完成深层特征提取.首先根据最小化重构误差准则构建基本子空间模型,并在构建过程中加入对称性、稀疏性约束.然后对基本子空间模型进行深度化改造,得到深层对称稀疏子空间模型.最后将各个层特征进行融合编码,得到深层特征提取结果.在人脸数据库及目标数据库上的实验表明,文中算法可以取得较高识别率及较好光照、表情、人脸朝向的鲁棒性.相比卷积神经网络等深度学习框架,文中算法具有结构简洁、收敛速度快等优点.%An algorithm of sparse feature extraction model is proposed based on deep and symmetric subspace learning.According to the theory of deep subspace learning, the constraints of symmetry and sparsity are introduced and the deep map network is built to extract features.Firstly, the basic subspace mapping matrix is constructed by minimizing the reconstruction error and the constraints of symmetry and sparsity are introduced for training.Next, the basic subspace model based on deep learning is reformed, and the deep symmetric sparse feature extraction model is built.These feature extraction results from different layers are merged to obtain the multi-layered deep symmetric subspace sparse feature.The experimental results on face databases show that the proposed algorithm achieves high recognition rates and strong robustness in illumination, expression and pose.Furthermore, compared with the convolutional neural networks, the proposed algorithm has the advantages of simple structure and high convergent rate.