A Fusion-based Gender Recognition Method Using Facial Images

TL;DR

This paper introduces a fusion-based facial gender recognition method combining multiple frameworks that utilize texture and geometric features, achieving a 94% accuracy on the FEI face dataset, comparable to state-of-the-art results.

Contribution

It presents a novel fusion approach integrating four different frameworks for improved gender recognition accuracy from facial images.

Findings

Achieved 94% recognition accuracy on FEI dataset.

Fusion of multiple frameworks enhances gender classification performance.

Method effectively combines texture and geometric facial features.

Abstract

This paper proposes a fusion-based gender recognition method which uses facial images as input. Firstly, this paper utilizes pre-processing and a landmark detection method in order to find the important landmarks of faces. Thereafter, four different frameworks are proposed which are inspired by state-of-the-art gender recognition systems. The first framework extracts features using Local Binary Pattern (LBP) and Principal Component Analysis (PCA) and uses back propagation neural network. The second framework uses Gabor filters, PCA, and kernel Support Vector Machine (SVM). The third framework uses lower part of faces as input and classifies them using kernel SVM. The fourth framework uses Linear Discriminant Analysis (LDA) in order to classify the side outline landmarks of faces. Finally, the four decisions of frameworks are fused using weighted voting. This paper takes advantage of both texture and geometrical information, the two dominant types of information in facial gender recognition. Experimental results show the power and effectiveness of the proposed method. This method obtains recognition rate of 94% for neutral faces of FEI face dataset, which is equal to state-of-the-art rate for this dataset.