基于结构化Fisherface的人脸识别新方法

特征提取是人脸识别中一个关键步骤。传统的Fisherface人脸识别方法中用样本的类均值和总体均值定义相应的散布矩阵，丢失了样本个体之间的结构信息，本文提出了一种基于原始样本个体结构信息的结构化Fisherface人脸识别方法，最后得到的特征数据中保留了原始样本更多的分布信息。在ORL人脸数据库的实验结果验证了该方法的有效性。

Classificador Fisherface Fuzzy para o reconhecimento de faces

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

基于模糊集理论的二维线性鉴别分析新方法

二维线性鉴别分析（2DLDA）是一种直接基于矩阵的特征提取方法，跳过传统的基于Fisher鉴别准则的线性鉴别分析方法中必须先将二维矩阵转化成一维矢量的过程，有效地提高了特征提取速度且避免了小样本问题，其识别率优于传统的Fisherface方法。结合模糊集理论，提出了一种新的2DLDA算法——模糊2DLDA（FIDLDA）算法。首先采用FKNN算法得到相应的样本分布信息，并按其对最后得到的特征向量所作的贡献融入到特征抽取过程中，得到有效的样本特征向量集。实验表明，F2DLDA算法的性能优于传统的2DLDA算法和Fisherface方法。

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.