Fusion of PCA-Based and LDA-Based Similarity Measures for Face Verification

The problem of fusing similarity measure-based classifiers is considered in the context of face verification. The performance of face verification systems using different similarity measures in two well-known appearance-based representation spaces, namely Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is experimentally studied. The study is performed for both manually and automatically registered face images. The experimental results confirm that our optimised Gradient Direction (GD) metric within the LDA feature space outperforms the other adopted metrics. Different methods of selection and fusion of the similarity measure-based classifiers are then examined. The experimental results demonstrate that the combined classifiers outperform any individual verification algorithm. In our studies, the Support Vector Machines (SVMs) and Weighted Averaging of similarity measures appear to be the best fusion rules. Another interesting achievement of the work is that although features derived from the LDA approach lead to better results than those of the PCA algorithm for all the adopted scoring functions, fusing the PCA- and LDA-based scores improves the performance of the system.