Combining multiple face recognition systems using Fisher's linear discriminant
Thomas Heseltine, Nick E. Pears, Jim Austin · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
The application of image processing as a pre-processing step to methods of face recognition can significantly improve recognition accuracy. However, different image processing techniques provide different advantages, enhancing specific features or normalising certain capture conditions. We introduce a new method of isolating these useful qualities from a range of image subspaces using Fisher's linear discriminant and combining them to create a more effective image subspace, utilising the advantages offered by numerous image processing techniques and ultimately reducing recognition error. Systems are evaluated by performing up to 258,840 verification operations on a large test set of images presenting typical difficulties when performing recognition. Results are presented in the form of error rate curves, showing false acceptance rate (FAR) vs. false rejection rate (FRR), generated by varying a decision threshold applied to the euclidean distance metric performed in combined face space.