Creating A New Color Space utilizing PSO and FCM to Perform Skin Detection by using Neural Network and ANFIS

TL;DR

This paper introduces a novel skin detection method using a new color space created with FCM and PSO algorithms, combined with neural networks and ANFIS, achieving higher accuracy than existing methods.

Contribution

It proposes a new color space for skin detection and compares linear and nonlinear classification methods across multiple color spaces, improving accuracy.

Findings

18.38% higher accuracy than previous methods

Achieved 90.05% 1-EER on COMPAQ dataset

Achieved 92.93% accuracy on Pratheepan dataset

Abstract

Skin color detection is an essential required step in various applications related to computer vision. These applications will include face detection, finding pornographic images in movies and photos, finding ethnicity, age, diagnosis, and so on. Therefore, proposing a proper skin detection method can provide solution to several problems. In this study, first a new color space is created using FCM and PSO algorithms. Then, skin classification has been performed in the new color space utilizing linear and nonlinear modes. Additionally, it has been done in RGB and LAB color spaces by using ANFIS and neural network. Skin detection in RBG color space has been performed using Mahalanobis distance and Euclidean distance algorithms. In comparison, this method has 18.38% higher accuracy than the most accurate method on the same database. Additionally, this method has achieved 90.05% in equal error rate (1-EER) in testing COMPAQ dataset and 92.93% accuracy in testing Pratheepan dataset, which compared to the previous method on COMPAQ database, 1-EER has increased by %0.87.