Eigenface Algorithm

We present a range of image processing techniques 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.

 The Face Database
 Detailed Results
 Results Summary

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