Face segmentation is the process of segmenting the visible parts of the face excluding the neck, ears, hair, and beards. In this field, several methods have been developed, but none of them have been effective in providing optimal face segmentation. Hence, we proposed a novel face segmentation method known as level-set-based neural network (NN) algorithm. This method exploits a hybrid filter for the pre-processing of images, which eliminates the unwanted noises and blurring effect from the images. The hybrid filter is the combination of Median, Mean, and Gaussian filters and effectively removes the unwanted noises. Hence the images are segmented by utilizing level-set-based NN algorithm which is commonly based on the population set and effectively reduces the gap between the predicted and expected outcomes. The proposed method is compared with state-of-art methods such as Fully convolution network (FCN), Gabor filter(GF), multi-class semantic face segmentation(MSFS), and genetic algorithms (GA). From the experimental analysis, it is evident that the proposed work achieved better results comparing to other approaches.