Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model

Rangayya,1*Email

Virupakshappa2 

Nagabhushan Patil3

1Department of Electronics & Communication Engineering, Sharnbasva University, Kalaburagi – 585103, Karnataka, INDIA.

2Department of Computer Science & Engineering, Sharnbasva University, Kalaburagi – 585103, Karnataka, INDIA. 19110, Jordan.

3Department of Electrical & Electronics Engineering, Poojya Doddappa Appa College of Engineering, Kalaburagi - 585103, Karnataka, INDIA.

Abstract

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.

Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model