PERFORMANCE EVALUATION OF DISTANCE METRICS: APPLICATION TO FINGERPRINT RECOGNITION
Distance metric is widely used in similarity estimation which plays a key role in fingerprint recognition. In this work we propose the detailed comparison of 29 distinct distance metrics. Features of fingerprint images are extracted using Fast Fourier Transform (FFT). Recognition rate, receiver operating curve (ROC), time and space complexity parameters are used for evaluation of each distance metric. To consolidate our conclusion we used the standard fingerprint database available at Bologna University and FVC2000 databases. After evaluation of 29 distinct distance metrics we found Sorgel distance metric performs best. Genuine acceptance rate (GAR) of Sorgel distance metric is observed to be similar to 5% higher than traditional Euclidean distance metric at low false acceptance rate (FAR). Sorgel distance gives good GAR at low FAR with moderate computational complexity.
fingerprint recognition fast fourier transform biometrics feature extraction dissimilarity measures