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In this paper, a novel method is proposed for face recognition based on pulse coupled neural network (PCNN) time signature. In this approach, a probe face is first extracted PCNN time signature as the recognition features, which a two-dimensional image is projected to a low one-dimensional feature space and then is classified based on the known samples. An extensive experimental investigation is conducted...
In this paper, a novel method is proposed to detect faces based on PCNN time signature and skin color segmentation, in which no training is needed. A test image is first divided into overlapped blocks and extracted PCNN time signature as the detection features, which a two-dimensional image is projected to a one-dimensional feature space. The test blocks are matched to a face template, which can be...
In this paper, a novel palmprint recognition approach is presented. A modified discrete cosine transform based feature extraction method is used to obtain palmprint features. Furthermore, a radial basis function neural network is employed for palmprint classification. In order to facilitate the training of radial basis function neural network, principal components analysis is applied to reduce these...
A forecast learning method of kernel principal component analysis (KPCA) is presented for specific emitter identification (SEI) application. By constructing a symmetrical decomposition of the kernel matrix, we derived a new algorithm of incremental KPCA. Based on it, the forecast capability is developed by creating dummy samples whose kernel vectors are an extrapolation of the kernel matrix. The advance...
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