Principal component analysis for minutiae verification on fingerprint image

Minutiae are the two most prominent and well-accepted classes of fingerprint features arising from local ridge discontinuities: ridge endings and ridge bifurcations. However, the preprocessing stage doesn't eliminate all possible defects in the original gray-level image and the orientation estimation in a poor image is extremely unreliable. In order to further eliminate the false minutiae caused by low quality, a minutiae verification mechanism is proposed to improve the identification per- formance. The minutiae verification mechanism uses the concept of eigen-codebook to find the optimal projection bases for true minutiae regions and false minutiae regions. Experimental results show that the performance is improved efficiently even less training data.