Comparison of FFT fingerprint filtering methods for neural network classification
P J Grother · 1994
Two types of Fourier Transform based filters are presented and used to enhance fingerprint images for use with a neural network fingerprint classification system developed at NIST [1][2].With image enhancement the system is capable of achieving classification error rates of 8.65% with 10% rejects (average over volumes 1-5 of NIST Special Database 9), a 2 percentage point improve- ment in error rate versus using no fingerprint enhancement.Speed of the filters range from 2 to 9 seconds.Classification tests were performed with fingerprints from NIST Special Database 9 Vol- umes 1 -5 [3] using ridge-valley based feature extraction, Karhunen Lo&ve transform, and a Proba- bilistic Neural Network (PNN) classifier.Improvements made to the classification system used include: a new segementor, use of non uniform feature vectors, and a faster version of the PNN classifier.The faster PNN classifier results in an average of four times faster classification with no change in resulting error rates.Also, the testing method used differs from past reports because no rolling of the same print is allowed to appear in both the training and testing set used by the Neural Network classifier.