Jin-gao Liu

Learn More
Two-dimensional principal component analysis (2DPCA) and two-dimensional linear discriminant analysis (2DLDA) are new techniques for face recognition. The main ideas behind 2DPCA and 2DLDA are that they are based on 2D matrices as opposed to the traditional PCA and LDA, which are based on 1D vector. In some literature, there has been a tendency to prefer(More)
—The paper proposes an effective algorithm for face recognition using face Gabor image and Support Vector Machine (SVM). The face Gabor image is firstly derived by downsampling and concatenating the Gabor wavelets representations which are the convolution of the face image with a family of Gabor kernels, and then the 2D Principle Component Analysis (2DPCA)(More)
An effective face recognition method is described in the proposed paper, which is based on Gabor Wavelets and 2D Linear Discriminant Analysis (Gabor-2DLDA). Although Gabor features has been recognized as one of the most successful face representations, its huge number of features often brings about the problem of curse of dimensionality. In this paper, we(More)
This paper describes the design and implementation of a synthesizable, flexible, alternative current-plasma display panel (AC-PDP) signal processor (APSP) as an intellectual property (IP) core for system-on-a-chip (SoC) application. The APSP adopts the Improved Address and Display Separation (ADS) method we proposed to overcome the dynamic false contour(More)
  • 1