Weighted Attribute Fusion Model for Face Recognition

TL;DR

This paper proposes a weighted attribute fusion model for face recognition that combines multiple feature extraction techniques with optimized weights to improve recognition accuracy.

Contribution

It introduces a novel weighted feature fusion approach that optimally combines various facial attribute features for enhanced face recognition performance.

Findings

Weighted feature fusion improves recognition accuracy.

Optimal weight selection enhances recognition rates.

Model tested on ORL dataset with positive results.

Abstract

Recognizing a face based on its attributes is an easy task for a human to perform as it is a cognitive process. In recent years, Face Recognition is achieved with different kinds of facial features which were used separately or in a combined manner. Currently, Feature fusion methods and parallel methods are the facial features used and performed by integrating multiple feature sets at different levels. However, this integration and the combinational methods do not guarantee better result. Hence to achieve better results, the feature fusion model with multiple weighted facial attribute set is selected. For this feature model, face images from predefined data set has been taken from Olivetti Research Laboratory (ORL) and applied on different methods like Principal Component Analysis (PCA) based Eigen feature extraction technique, Discrete Cosine Transformation (DCT) based feature extraction technique, Histogram Based Feature Extraction technique and Simple Intensity based features. The extracted feature set obtained from these methods were compared and tested for accuracy. In this work we have developed a model which will use the above set of feature extraction techniques with different levels of weights to attain better accuracy. The results show that the selection of optimum weight for a particular feature will lead to improvement in recognition rate.