Robust least squares projection twin SVM and its sparse solution

Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly.

3D human face reconstruction based on band-limited binary patterns

Face recognition technology has great prospects for practical applications. Three-dimensional（3D） human faces are becoming more and more important in consideration of the limits of two-dimensional face recognition. We propose an active binocular setup to obtain a 3D colorful human face using the band-limited binary patterns（BBLP） method. Two grayscale cameras capture the BBLP projected onto the target of human face by a digital light processing（DLP） projector synchronously. Then, a color camera captures a colorful image of the human face. The benefit of this system is that the 3D colorful human face can be obtained easily with an improved temporal correlation algorithm and the precalibration results between three cameras. The experimental results demonstrated the robustness, easy operation, and the high speed of this 3D imaging setup.