This paper addresses the problem of dense disparity estimation from a pair of color stereo images. Based on a convex set theoretic formulation, the stereo matching problem is cast as a convex programming problem in which a color-based objective function is minimized under specific convex constraints. These constraints arise from prior knowledge and rely on various properties of the disparity field to be estimated. The resulting multi-constrained optimization problem is solved via an efficient parallel block-iterative algorithm. Four different color spaces have been tested in order to evaluate their suitability for stereo matching. Experiments on standard stereo images show that the matching results have been efficiently improved when using color information instead of grey values.