We all know analyzing multivariate mixtures can be influenced by outliers/extreme observations in the data. Many methods have been studied in the literature which are resistant to outliers. When the number of outlying objects become, larger and have similar or different type distribution from a major part of the data, they can be considered as another component from the mixture distribution and in this case parameter estimates can be compared with the proposed robust methods. On the other hand, when the number of outlying objects is smaller, we should be able to detect the major part of the data. Finding estimates which are highly efficient when there is no data contamination and at the same time, high resistance to outliers, i.e. provide lower bias is not always an easy task. In this study, we plan to perform a comparison study of robustness based on minimum integrated square error estimation (L2E) including the partial L2E , with the well-known  alternatives including EM algorithm and other multivariate covariance and location m-estimates. We compare our  performance  with multiple simulation study with different types of data contamination.