Makeup-Invariant Faces Recognition Using a Pre-Trained Neural Network, Grasshopper Optimization Algorithm, and Random Forest

Abstract

Makeup-Invariant Face Recognition is a critical area of research that aims to identify and classify manipulated or forged face images in the field of face recognition systems. Advances in deep learning techniques, such as pre-trained neural networks, have shown promising results in this domain. In this paper, a novel approach for detecting manipulated faces is proposed, combining the strengths of pre-trained neural networks, the Grasshopper Optimization (GOA) Algorithm, and Random Forest classifier. The proposed method utilizes the powerful feature extraction capabilities of pre-trained deep neural networks to capture intricate details and patterns in face images. Subsequently, the GOA algorithm is employed to select optimal features. Additionally, the Random Forest classifier is utilized for effective classification of face images and identifying different individuals based on the selected features. By integrating these three algorithms, the proposed approach demonstrates significant improvements in detecting manipulated faces, achieving higher detection rates and lower false positive rates compared to existing state of the art methods. Simulation results on data from 25 different individuals with manipulated face images show an average accuracy of 97.23%, which represents an enhancement over the compared methods.