Interactive evolutionary image processing for face beautification using smaller population size

A method to improve image processing with interactive evolutionary computing (IEC) is proposed in order to get a satisfactory output image with smaller population size. IEC is a powerful method for designing a system on the basis of human subjective criteria. Image processing with IEC is effective to process images considering human subjective criteria and taste, however, it requires a large PC system to show enough number of candidate output images on a large display; this feature makes this method difficult to be realized on a small mobile device. Here, the number of the candidates corresponds to the population size in the genetic algorithm (GA) in IEC. If the number of candidate output images is restricted, IE image processing takes more iteration time to get satisfactory result from small number of candidates. In order to solve this problem, a method to improve IEC is proposed so that the initial population is designed effectively by clustering past optimized parameters, and the generation change is modified by considering the significance of the first choice. This method is applied to human face image beautifying system, and the experimental results show that users can get more satisfactory result with smaller number of iterations.