Qin Shi

Beijing University of Technology, Peping, Beijing, China

Are you Qin Shi?

Claim your profile

Publications (4)0 Total impact

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A novel nonlinear face recognition method named GPPface is proposed in this paper. GPPface is based on nonlinear dimensionality reduction algorithm, geodesic preserving projection (GPP). As face images are regarded to be embedded in a nonlinear space, GPP is presented to nonlinearly map high-dimensional face images to low-dimensional feature space. GPP overcomes the weaknesses of traditional linear and nonlinear dimensionality reduction algorithms, well preserves the intrinsic structure of the manifold and can fast and efficiently map new sample point to feature space. To recover space structure of face images and tackle small sample size problem, 3D morphable model is developed to derive multiple images of a person from a single image. Experimental results on ORL and PIE face databases show that our method makes impressive performance improvement compared with conventional face recognition methods
    Cognitive Informatics, 2006. ICCI 2006. 5th IEEE International Conference on; 08/2006
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A novel model matching method based on genetic algorithm is presented in this paper for 3D face reconstruction. Having constructed the morphable model, genetic algorithm is proposed to tackle model matching problem. Multi-lights illumination model is developed to fit for more complex conditions. New model matching method based on genetic algorithm is independent from initial values and more robust than stochastic gradient descent method. Crossover and mutation probability are regulated during optimization process to improve precision and convergence speed of the algorithm. Multi-lights illumination model improves the stability of 3D face reconstruction and ability to evaluate illumination conditions of input facial images. Experimental results show the proposed method has good performance on 3D face reconstruction
    Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on; 01/2006
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This present study, proposes a new 3D face correspondence method. A uniform mesh re-sampling algorithm is combined with mesh simplification algorithm to make correspondence between vertices of prototypical 3D faces. Uniform mesh re-sampling algorithm is developed to obtain the same topology between 3D faces with different structures. A global error metrics is proposed and mesh simplification is implemented on 3D faces with same topologies simultaneously. The new method overcomes the limitation of conventional uniform mesh re-sampling and optical flow algorithm, decreases the vertices, and triangles that need to represent 3D face while preserving correspondence between vertices of the prototypes. The experimental results show the new method gives good performance on computing 3D face correspondence.
    01/2005;
  • [Show abstract] [Hide abstract]
    ABSTRACT: An improved adaptive principal component extraction algorithm is proposed in this present study to overcome the high algorithm complexity and high computing complexity that exists in the present algorithms. The computing complexity is decreased by improving the update equation of feed-forward network weight value. Parallel algorithm of the improved adaptive principal component extraction is also presented. The experimental results on ORL face database show the improved adaptive principal component extraction algorithm is efficient in the facial feature extraction.