Snakes or active contours are widely used for image segmentation. There are many different implementations of snakes. No matter which implementation is being employed, the segmentation results suffer greatly in presence of occlusions, noise, concavities or abnormal modification of shape. If some prior knowledge about the shape of the object is available, then its addition to an existing model can greatly improve the segmentation results. In this work inclusion of such shape constraints for explicit active contours is presented. These shape priors are introduced through the use of Fourier based descriptors which makes them invariant to the translation, scaling and rotation factors and enables the deformable model to converge towards the prior shape even in the presence of occlusion and context noise. These shape constraints have been computed in descriptor space so no reconstruction is required and more generic descriptors can be used. Experimental results clearly indicate that the inclusion of these shape priors greatly improved the segmentation results in comparison with the original snake model.