Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine RidgeStructure Dictionary

Abstract

Abstract-Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly 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 this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate ("lights-out" capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving "lights-out" latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L 1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher.

Learning Fingerprint Reconstruction: From Minutiae to Image

“…where s(·, ·) is defined in (15) and σ f is a parameter controlling the frequency similarity (σ f = 3 in this paper). The minutiae can be added by combining the continuous phase patch and the spiral phase computed from the minutiae in a patch.…”

“…An important reason for this loss of matching performance is that no prior knowledge of fingerprint ridge structure was utilized in these reconstruction approaches to reproduce the fingerprint characteristics. In the literature, such prior knowledge has been represented in terms of using orientation patch dictionary [14] and ridge structure dictionary [15] for latent segmentation and enhancement. In this paper, our goal is to utilize a similar dictionary-based approach to improve the fingerprint reconstruction from a given minutiae set.…”

Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency

“…Thus, it is still challenging for segmentation of latent fingerprints. There are some research efforts made to investigate new methods for improving the latent fingerprint segmentation [2][3][4][9][10][11][12][13][14][15]. Zhang et al [2] proposed an adaptive directional total variation (ADTV) model for latent fingerprint segmentation.…”

“…Karimi-Ashtiani and Jay Kuo [11] proposed a latent fingerprint segmentation algorithm based on a quasi-global fingerprint model, which did not rely on the local gradients for estimation of orientations and frequencies and thus is robust to the gradient deviations. Cao et al [12] proposed a method for latent fingerprint segmentation based on a ridge quality measure which was defined as the structural similarity between the fingerprint patch and its dictionary based reconstruction. A learning based method was proposed for latent fingerprint image segmentation based on fractal dimension features and weighted extreme learning machine ensemble [3].…”

Fingerprint image analysis: role of orientation patch and ridge structure dictionaries

“…In this chapter, we investigate the use of dictionaries for the challenging problems in latent fingerprint image analysis, namely latent fingerprint segmentation and enhancement. Given that fingerprint patterns can be represented at two different levels (i.e., coarse representation for fingerprint ridge flow or orientation field, and fine representation for ridges and valleys), two dictionaries are developed: an orientation patch 2 dictionary (Feng et al 2013) and a ridge structure dictionary (Cao et al 2014). An orientation patch dictionary, which contains only the orientation information in patches, is proposed to estimate the orientation field for latent fingerprint enhancement.…”