Abstract: A class of new infinitely many independence measures named quasi-entropy (QE) is proposed, in which strictly convex functions are used to evaluate the uniformity of the joint probability of the variables.In QE, none of priori assumptions is made on the shape or statistical features of the distribution functions of continuous variables but unbiased estimates of the values of distribution functions are obtained from the samples.Therefore, blind separation algorithms based on QE can separate signals with arbitrary continuous distributions, including those with zero kurtoses.The superior performance of the QE-based algorithms is verified by comparison experiments with previous algorithms.