An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimens...An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.展开更多
文摘汉语组块分析是中文信息处理领域中一项重要的子任务.在一种新的结构化SVMs(support vector machines)模型的基础上,提出一种基于大间隔方法的汉语组块分析方法.首先,针对汉语组块分析问题设计了序列化标注模型;然后根据大间隔思想给出判别式的序列化标注函数的优化目标,并应用割平面算法实现对特征参数的近似优化训练.针对组块识别问题设计了一种改进的F1损失函数,使得F1损失值能够依据每个句子的实际长度进行相应的调整,从而能够引入更有效的约束不等式.通过在滨州中文树库CTB4数据集上的实验数据显示,基于改进的F1损失函数所产生的识别结果优于Hamming损失函数,各种类型组块识别的总的F1值为91.61%,优于CRFs(conditional random fields)和SVMs方法.
文摘An efficient face representation is a vital step for a successful face recognition system. Gabor features are known to be effective for face recognition. The Gabor features extracted by Gabor filters have large dimensionality. The feature of wavelet transformation is feature reduction. Hence, the large dimensional Gabor features are reduced by wavelet transformation. The discriminative common vectors are obtained using the within-class scatter matrix method to get a feature representation of face images with enhanced discrimination and are classified using radial basis function network. The proposed system is validated using three face databases such as ORL, The Japanese Female Facial Expression (JAFFE) and Essex Face database. Experimental results show that the proposed method reduces the number of features, minimizes the computational complexity and yielded the better recognition rates.
