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.
We present a range of image processing tecimiques as potential pre-processing steps, which attempt to improve the performance of the eigenface method of face recognition. Verification tests are carried out by applying thresholds to gather false acceptance rate (FAR) and false rejection rate (FRR) results from a data set comprised of images that present typical difficulties when attempting recognition, such as strong variations in lighting direction and intensity, partially covered faces and changes in facial expression. Results are compared using the equal error rate (EER), which is the error rate when FAR is equal to FRR. We determine the most successful methods of image processing to be used with eigenface based face recognition, in application areas such as security, surveillance, data compression and archive searching.
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