Titlebook: Advances in Neural Networks - ISNN 2006; Third International Jun Wang,Zhang Yi,Hujun Yin Conference proceedings 2006 Springer-Verlag Berli

PIZZA

Qilian Liang,Xin Liu,Baoju Zhangssing and pattern recognition, and it plays a very important role in the applications of human-computer interfaces and human emotion analysis. This paper proposes an algorithm called BoostingTree, which is based on the conventional Adaboost and uses tree-structure to convert seven facial expressions

陶瓷

油氈

Zhao Zhang,Baoju Zhang,Hongwei Liu,Bo Zhangtes the capacities of biological brains for signal processing in pattern recognition. Its accuracy and efficiency are demonstrated in this report on an application to human face recognition, with comparisons of performance with conventional pattern recognition algorithms.

機(jī)警

Liantao Bai,Yaxing Liu,Jun Wang,Hengpeng Xuhere a quick human “glance” is sufficient to recognize a “familiar” face. Face recognition has recently attracted more research aimed at developing reliable recognition by machines. Current face recognition methods rely on detecting certain features within a face and using these features for face re

labile

https://doi.org/10.1007/978-981-99-1260-5 of both labeled and unlabeled data. In face recognition, collecting labeled examples costs human effort, while vast amounts of unlabeled data are often readily available and offer some additional information. In this paper, based on Support Vector Machine (SVM), we introduce a novel semi-supervised

宿醉

Zheming Sun,Chunjuan Bo,Dong Wang function (RBF) classifier. NMFs can represent a facial image based on either local or holistic features by constraining the sparseness of the basis images. The comparative experiments are carried out between NMFs with low or high sparseness and principle component analysis (PCA) for recognizing fac

peptic-ulcer

Yukun Wang,Wenbin Guo,Xiao Zhangre subspaces learned by principal component analysis (PCA). The two classifiers employ the same classification model named a polynomial neural network (PNN). The outputs of the two classifiers are fused to make the final decision. The effectiveness of the proposed method has been demonstrated in exp

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