基于局部融合特征与分层增量树的快速人脸识别算法

传统的人脸识别模型采用离线方式进行训练,同时由于人脸特征维数较高导致算法的实时性不足。文中分别从人脸特征与分类器两方面来构建快速的人脸识别算法。首先使用 SDM(Supervised Descent Method)算法进行人脸特征点定位,提取每个人脸特征点邻域内的局部(Multi Block-Center Symmetric Local Binary Patterns,MB-CSLBP)特征,并将所有的人脸特征点邻域特征以串联的方式构成局部融合特征,即所提出的局部融合MB-CSLBP特征LFP-MB-CSLBP(Local Fusion Feature of MB-CSLBP)。将以上特征送入分层增量树HI-tree(Hierarchical Incremental tree)中进行人脸识别模型的在线训练。分层增量树是使用分层聚类算法来实现增量式学习的,因此其能够以在线的方式对识别模型进行训练,具有较高的实时性与准确性。最后在3种不同的人脸库以及摄像头采集的人脸视频上对算法的识别率与实时性进行测试。实验结果表明,相比于当前其他算法,所提算法具有较高的人脸识别率与实时性。

Fast Face Recognition Algorithm Based on Local Fusion Feature and Hierarchical Incremental Tree

The off-line training and the high dimension of facial features in face recognition lead to the difficulty of achieving real-time processing performance.To solve this problem,the local fusion features and the hierarchical incremental tree were applied to construct a fast face recognition algorithm.Firstly,the supervised descent method(SDM) is used to locate the facial feature points.The feature of multi block-center symmetric local binary patterns(MB-CSLBP) in the neighborhood of each facial feature point is extracted and fused in series,which constitutes the proposed facial feature of local fusion feature of MB-CSLBP(LFP-MB-CSLBP).Then the above facial feature is sent into hierarchical incremental tree(HI-tree).Because the hierarchical clustering algorithm is used in the HI-tree to achieve incremental learning,it can train the recognition model online.Finally,the recognition rate and consuming time of the proposed algorithm are tested on three face databases and real application of video-based face recognition.The experimental results show that the proposed algorithm has better real-time computation and accuracy compared with other current approaches.