The direct use of the entire photometric image information as dense feature for visual servoing brings several advantages. First, it does not require any feature detection, matching or tracking process. Thanks to the redundancy of visual information, the precision at convergence is really accurate. However , the corresponding highly nonlinear cost function reduces the convergence domain. In this paper, we propose a visual servoing based on the analytical formulation of Gaussian mixtures to enlarge the convergence domain. Pixels are represented by 2D Gaussian functions that denotes a "power of attraction". In addition to the control of the camera velocities during the servoing, we also optimize the Gaussian spreads allowing the camera to precisely converge to a desired pose even from a far initial one. Simulations show that our approach outperform the state of the art and real experiments show the effectiveness, robustness and accuracy of our approach.