An effective fingerprint image segmentation based on multi-features histogram analysis is presented. We extract a new
feature, together with three other features to segment fingerprints. Two of these four features, each of which is related to
one of the other two, are reciprocals with each other, so features are divided into two groups. These two features'
histograms are calculated respectively to determine which feature group is introduced to segment the aim-fingerprint.
The features could also divide fingerprints into two classes with high and low quality. Experimental results show that our
algorithm could classify foreground and background effectively with lower computational cost, and it can also reduce
pseudo-minutiae detected and improve the performance of AFIS.