Next Level of Data Fusion for Human Face Recognition

TL;DR

This paper explores data and decision fusion techniques in human face recognition, combining visual and thermal images with wavelet-based fusion, PCA, neural networks, and Bayesian decision fusion to improve recognition accuracy.

Contribution

It introduces a dual-level fusion approach integrating wavelet-based data fusion and Bayesian decision fusion, enhancing face recognition performance.

Findings

Fusion improves recognition accuracy over single classifiers

Wavelet domain fusion effectively combines thermal and visual data

Decision fusion with Bayesian approach enhances robustness

Abstract

This paper demonstrates two different fusion techniques at two different levels of a human face recognition process. The first one is called data fusion at lower level and the second one is the decision fusion towards the end of the recognition process. At first a data fusion is applied on visual and corresponding thermal images to generate fused image. Data fusion is implemented in the wavelet domain after decomposing the images through Daubechies wavelet coefficients (db2). During the data fusion maximum of approximate and other three details coefficients are merged together. After that Principle Component Analysis (PCA) is applied over the fused coefficients and finally two different artificial neural networks namely Multilayer Perceptron(MLP) and Radial Basis Function(RBF) networks have been used separately to classify the images. After that, for decision fusion based decisions from both the classifiers are combined together using Bayesian formulation. For experiments, IRIS thermal/visible Face Database has been used. Experimental results show that the performance of multiple classifier system along with decision fusion works well over the single classifier system.