Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared torolled and plain fingerprint matching, latent fingerprint identification accuracy is much lower due to complex background noise,poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g.,region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce thismarkup cost and to improve the consistency in feature markup, fully automatic and highly accurate latentmatching algorithms are needed. In this paper, we propose an automatic region segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. It utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed region segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region.
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