NIST SD27 Latent Fingerprint Database Research Articles

Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List

Efficiently identifying the mated gallery fingerprint of a latent fingerprint in a large database requires a highly accurate and efficient fingerprint matching algorithm. The common strategy to achieve this goal is to combine an efficient indexing algorithm with a slow but accurate matching algorithm. Despite of the importance of latent indexing, it has received far less attention than rolled and plain fingerprint indexing. Due to the small fingerprint area, poor image quality and huge variety in information quantity of latent fingerprints, existing rolled and plain fingerprint indexing approaches cannot be simply migrated to the latent fingerprint indexing. In this paper, we propose (1) a multi-scale fixed-length representation approach for latent fingerprint indexing, and (2) a fingerprint information quantity estimation approach for adaptive candidate list reduction. The representation scheme is designed to deal with small finger area and low image quality of latents. The information quantity of a latent is a predictor of the indexing score of its mated gallery fingerprint and thus can be used to determine a proper threshold for its candidate list. Extensive experimental results on NIST SD27, MOLF, N2N, and Hisign latent fingerprint databases show that the proposed method achieved the state-of-the-art indexing accuracy on latent fingerprints, and significantly improved the efficiency of state-of-the-art latent matching algorithm.

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement.

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

Automatic Segmentation and Enhancement of Latent Fingerprints Using Deep Nested UNets

Latent fingerprints are one of the most important evidences used to identify criminals in the law enforcement and forensic agencies. Automated recognition of latent fingerprints is still challenging due to their poor image quality caused by unclear ridge structure and various overlapping patterns. Segmentation and enhancement are important to identify valid fingerprint regions, reduce the noise and improve the clarity of ridge structure for more accurate fingerprint recognition. In this paper, we propose a deep convolutional neural network architecture with the nested UNets for automatic segmentation and enhancement of latent fingerprints. First, to prepare training data, we synthetically generate the latent fingerprints and their segmentation and enhancement ground truth data for training. Then, a deep architecture of nested UNets is proposed to transform low-quality latent image into the segmentation mask and high-quality fingerprint through the pixels-to-pixels and end-to-end training. Finally, the test latent fingerprint is segmented and enhanced with the deep nested UNets to improve the image quality in one shot. The enhancement network is optimized by combining the local and global losses, which not only helps reconstruct the global structure, but also enhance the local ridge details of latent fingerprints. The proposed network can make use of multi-level feature maps in a pyramid way of nested UNets for segmentation and enhancement. Experimental results and comparison on NIST SD27 and IIITD-MOLF latent fingerprint databases demonstrate the promising performance of the proposed method.

CNNAI: A Convolution Neural Network-Based Latent Fingerprint Matching Using the Combination of Nearest Neighbor Arrangement Indexing.

Automatic fingerprint identification systems (AFIS) make use of global fingerprint information like ridge flow, ridge frequency, and delta or core points for fingerprint alignment, before performing matching. In latent fingerprints, the ridges will be smudged and delta or core points may not be available. It becomes difficult to pre-align fingerprints with such partial fingerprint information. Further, global features are not robust against fingerprint deformations; rotation, scale, and fingerprint matching using global features pose more challenges. We have developed a local minutia-based convolution neural network (CNN) matching model called “Combination of Nearest Neighbor Arrangement Indexing (CNNAI).” This model makes use of a set of “n” local nearest minutiae neighbor features and generates rotation-scale invariant feature vectors. Our proposed system doesn't depend upon any fingerprint alignment information. In large fingerprint databases, it becomes very difficult to query every fingerprint against every other fingerprint in the database. To address this issue, we make use of hash indexing to reduce the number of retrievals. We have used a residual learning-based CNN model to enhance and extract the minutiae features. Matching was done on FVC2004 and NIST SD27 latent fingerprint databases against 640 and 3,758 gallery fingerprint images, respectively. We obtained a Rank-1 identification rate of 80% for FVC2004 fingerprints and 84.5% for NIST SD27 latent fingerprint databases. The experimental results show improvement in the Rank-1 identification rate compared to the state-of-art algorithms, and the results reveal that the system is robust against rotation and scale.

Automatic latent fingerprint identification system using scale and rotation invariant minutiae features

In this paper, we propose a new clustered minutiae-based scale and rotation invariant fingerprint matching method. The major challenge faced in the existing latent fingerprint identification system is the lack of minutiae features in the fingerprint regions and hence there is a requirement to utilize the existing minutiae arrangements in the regions to identify the query fingerprint. We have clustered minutiae around a reference minutia and generated minutiae invariants to identify the fingerprint. In this paper, we propose two algorithms based on the minutiae neighborhood. To solve the geometrical constraints between the pairs of nearest points around a minutia, we propose the latent minutiae similarity (LMS) algorithm. Based on geometrical arrangements on the set of latent minutiae patterns around a minutia, we propose a clustered latent minutiae pattern (CLMP) algorithm. We test our algorithms on the FVC2004 and NIST SD27 criminal fingerprint databases. Proposed LMS, CLMP algorithms produced the highest 97.5% and 100% of Rank-1 identification accuracy respectively on plain FVC2004 dataset. Whereas, for NIST SD27 latent fingerprint database the proposed LMS, CLMP algorithms produced the highest Rank-1 identification accuracy of 88.8% and 93.80% respectively. Experimental results show significant improvement in the Rank-1 matching accuracy under random fingerprint scale and rotation condition compared to the state-of-the-art algorithms.

Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network

Dense registration of fingerprints is a challenging task due to elastic skin distortion, low image quality, and self-similarity of ridge pattern. To overcome the limitation of handcraft features, we propose to train an end-to-end network to directly output pixel-wise displacement field between two fingerprints. The proposed network includes a siamese network for feature embedding, and a following encoder-decoder network for regressing displacement field. By applying displacement fields reliably estimated by tracing high quality fingerprint videos to challenging fingerprints, we synthesize a large number of training fingerprint pairs with ground truth displacement fields. In addition, based on the proposed registration algorithm, we propose a fingerprint mosaicking method based on optimal seam selection. Registration and matching experiments on FVC2004 databases, Tsinghua Distorted Fingerprint (TDF) database, and NIST SD27 latent fingerprint database show that our registration method outperforms previous dense registration methods in accuracy and efficiency. Mosaicking experiment on FVC2004 DB1 demonstrates that the proposed algorithm produced higher quality fingerprints than other algorithms which also validates the performance of our registration algorithm.

Progressive Focusing Algorithm for Reliable Pose Estimation of Latent Fingerprints

A pose of fingerprint composes of a reference point and its corresponding orientation. The reliable pose plays a crucial role in fingerprint alignment and registration. Pose estimation of latent fingerprints is a challenging problem. A few methods based on machine learning were proposed in the past decade. These methods try to predict a pose from corrupted information. In this paper, we propose a systematic feedback approach which can remedy the corrupted information while simultaneously estimates a reliable pose. Without manual segmentation, our fully automatic algorithm is able to progressively locate potential poses, and enhance weak friction ridges that form and support these poses through an iterative feedback. Using the NIST SD27 and MOLF DB4 latent fingerprint databases, our experimental results show that our proposed algorithm outperforms the state-of-the-art and existing commercial products for latent fingerprint pose estimation in terms of precision and identification accuracy.

Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency

Latent fingerprints are captured from the fingerprint impressions left unintentionally at the surfaces of the crime scene. They are often used as an important evidence to identify criminals in law enforcement agencies. Different from the widely used plain and rolled fingerprints, the latent fingerprints are usually of poor quality consisting of complex background with a lot of nonfingerprint patterns and various noises. Latent fingerprint segmentation is an important image processing step to separate fingerprint foreground from background for more accurate and efficient feature extraction and matching. Traditional methods are usually based on the local features such as gray scale variance and gradients, which are sensitive to noise and cannot work well for latent images. This paper proposes a latent fingerprint segmentation method based on combination of ridge density and orientation consistency, which are global and local features of fingerprints, respectively. First, a texture image is obtained by decomposition of latent image with a total variation model. Second, we propose to detect the ridge segments from the texture image, and then compute the density of ridge segments and ridge orientation consistency to characterize the global and local fingerprint patterns. Finally, fingerprint segmentation is performed by combining the ridge density and orientation consistency for latent images. The proposed method has been evaluated on NIST SD27 latent fingerprint database. Experimental results and comparison demonstrate the promising performance of the proposed method.

P.3.b.043 - Two-year follow-up of patients with a first psychotic episode: comparison between affective and non-affective psychoses and predictors of functioning

Dense registration of different impressions of the same finger is beneficial to various fingerprint matching methods. This is a challenging problem due to elastic distortion of finger skin and sparsity of distinctive features (namely minutiae) in fingerprints. Most existing fingerprint registration algorithms produce only correspondences between minutiae, which are not sufficient for dense registration of fingerprints. In this paper, we proposed a novel dense fingerprint registration algorithm, which consists of a composite initial registration step and a dual-resolution block-based registration step. The dual-resolution block-based registration is approached in an energy minimization framework which consists of local search, energy function construction and global optimization. In local search step, a candidate set of transformations of every input image block are found using image correlation w.r.t. the corresponding reference image block. In energy function construction, two factors are considered: (1) the similarity between the transformed input block and the corresponding reference block, and (2) the compatibility between transformations of neighboring input blocks. In global optimization, a region growing style algorithm is proposed to minimize the energy function. Experimental results on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database and NIST SD27 latent fingerprint database, show that the proposed algorithm not only produces more accurate registration results but also improves the matching performance by fusion of minutiae matching and image correlation.

Sparse coding based orientation estimation for latent fingerprints

Fingerprint orientations are often used to describe the ridge flow patterns, providing useful features for further fingerprint processing and recognition. Although significant advances have been achieved for orientation estimation, it is still challenging to reliably estimate the orientations for latent fingerprints, which are usually of poor quality with unclear ridge structure and various overlapping patterns. Motivated by the recent success of sparse coding in image denoising and reconstruction, this paper proposes an orientation estimation algorithm based on dictionary learning and sparse coding for latent fingerprints. First, a texture image is obtained by decomposition of latent image with a total variation model. The structured noise is greatly reduced from the texture image. Second, we propose a multi-scale sparse coding method for iterative estimation of local ridge orientations on the texture image. Multi-scale dictionaries are learned from the orientation fields of good quality fingerprints to capture the prior knowledge of various orientation patterns, and sparse coding is iteratively applied with the increase of patch sizes to correct the corrupted orientations of latent fingerprint. The proposed algorithm can work well to reduce the effect of various noise and restore the corrupted orientations while maintain the details of singular region. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm.

Dense registration of fingerprints

Dense registration of different impressions of the same finger is beneficial to various fingerprint matching methods. This is a challenging problem due to elastic distortion of finger skin and sparsity of distinctive features (namely minutiae) in fingerprints. Most existing fingerprint registration algorithms produce only correspondences between minutiae, which are not sufficient for dense registration of fingerprints. In this paper, we proposed a novel dense fingerprint registration algorithm, which consists of a composite initial registration step and a dual-resolution block-based registration step. The dual-resolution block-based registration is approached in an energy minimization framework which consists of local search, energy function construction and global optimization. In local search step, a candidate set of transformations of every input image block are found using image correlation w.r.t. the corresponding reference image block. In energy function construction, two factors are considered: (1) the similarity between the transformed input block and the corresponding reference block, and (2) the compatibility between transformations of neighboring input blocks. In global optimization, a region growing style algorithm is proposed to minimize the energy function. Experimental results on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database and NIST SD27 latent fingerprint database, show that the proposed algorithm not only produces more accurate registration results but also improves the matching performance by fusion of minutiae matching and image correlation.

Detection and Rectification of Distorted Fingerprints

Elastic distortion of fingerprints is one of the major causes for false non-match. While this problem affects all fingerprint recognition applications, it is especially dangerous in negative recognition applications, such as watchlist and deduplication applications. In such applications, malicious users may purposely distort their fingerprints to evade identification. In this paper, we proposed novel algorithms to detect and rectify skin distortion based on a single fingerprint image. Distortion detection is viewed as a two-class classification problem, for which the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to perform the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression problem, where the input is a distorted fingerprint and the output is the distortion field. To solve this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of the input fingerprint is found in the reference database and the corresponding distortion field is used to transform the input fingerprint into a normal one. Promising results have been obtained on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database, and the NIST SD27 latent fingerprint database.

Latent Fingerprint Enhancement via Multi-Scale Patch Based Sparse Representation

Latent fingerprint identification plays an important role for identifying and convicting criminals in law enforcement agencies. Latent fingerprint images are usually of poor quality with unclear ridge structure and various overlapping patterns. Although significant advances have been achieved on developing automated fingerprint identification system, it is still challenging to achieve reliable feature extraction and identification for latent fingerprints due to the poor image quality. Prior to feature extraction, fingerprint enhancement is necessary to suppress various noises, and improve the clarity of ridge structures in latent fingerprints. Motivated by the recent success of sparse representation in image denoising, this paper proposes a latent fingerprint enhancement algorithm by combining the total variation model and multiscale patch-based sparse representation. First, the total variation model is applied to decompose the latent fingerprint into cartoon and texture components. The cartoon component with most of the nonfingerprint patterns is removed as the structured noise, whereas the texture component consisting of the weak latent fingerprint is enhanced in the next stage. Second, we propose a multiscale patch-based sparse representation method for the enhancement of the texture component. Dictionaries are constructed with a set of Gabor elementary functions to capture the characteristics of fingerprint ridge structure, and multiscale patch-based sparse representation is iteratively applied to reconstruct high-quality fingerprint image. The proposed algorithm cannot only remove the overlapping structured noises, but also restore and enhance the corrupted ridge structures. In addition, we present an automatic method to segment the foreground of latent image with the sparse coefficients and orientation coherence. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm and its superiority over existing algorithms.

Latent Fingerprints Segmentation: Feasibility of Using Clustering-Based Automated Approach

Latent fingerprints are the impressions of ridges left on a crime scene due to unintentional touching of criminal’s fingers on different objects. These impressions of ridges have been used as vital evidences for identifying the criminals by law enforcement agencies. In this study, an attempt for automated segmentation of latent fingerprint has been made using K-means clustering. In proposed approach, Sobel filter and morphological operations have been used for background evaluation and image enhancement. Clustering is used to classify image data into k clusters to separate the foreground and background information. Mask has been generated on the basis of clustered data and is used to obtain the segmentation of latent fingerprints. Simulation results of the proposed algorithm show significant improvement in terms of missed detection rate (MDR) and false detection rate (FDR) using NIST SD-27 latent fingerprint database. Segmentation results reveal the MDR of 1.80, 4.75 and 7.80% and FDR of 17.85, 26.28 and 34.05% for good, bad and ugly quality of latent fingerprints, respectively. Moreover, visual segmentation reliability (VSR) is upto 90% for good quality images and varies in the range of 70–80% for bad quality latent fingerprints, whereas VSR for ugly fingerprints is in the range of 50–60%.

Adaptive Directional Total-Variation Model for Latent Fingerprint Segmentation

A new image decomposition scheme, called the adaptive directional total variation (ADTV) model, is proposed to achieve effective segmentation and enhancement for latent fingerprint images in this work. The proposed model is inspired by the classical total variation models, but it differentiates itself by integrating two unique features of fingerprints; namely, scale and orientation. The proposed ADTV model decomposes a latent fingerprint image into two layers: cartoon and texture. The cartoon layer contains unwanted components (e.g., structured noise) while the texture layer mainly consists of the latent fingerprint. This cartoon-texture decomposition facilitates the process of segmentation, as the region of interest can be easily detected from the texture layer using traditional segmentation methods. The effectiveness of the proposed scheme is validated through experimental results on the entire NIST SD27 latent fingerprint database. The proposed scheme achieves accurate segmentation and enhancement results, leading to improved feature detection and latent matching performance.

Orientation Field Estimation for Latent Fingerprint Enhancement

Identifying latent fingerprints is of vital importance for law enforcement agencies to apprehend criminals and terrorists. Compared to live-scan and inked fingerprints, the image quality of latent fingerprints is much lower, with complex image background, unclear ridge structure, and even overlapping patterns. A robust orientation field estimation algorithm is indispensable for enhancing and recognizing poor quality latents. However, conventional orientation field estimation algorithms, which can satisfactorily process most live-scan and inked fingerprints, do not provide acceptable results for most latents. We believe that a major limitation of conventional algorithms is that they do not utilize prior knowledge of the ridge structure in fingerprints. Inspired by spelling correction techniques in natural language processing, we propose a novel fingerprint orientation field estimation algorithm based on prior knowledge of fingerprint structure. We represent prior knowledge of fingerprints using a dictionary of reference orientation patches. which is constructed using a set of true orientation fields, and the compatibility constraint between neighboring orientation patches. Orientation field estimation for latents is posed as an energy minimization problem, which is solved by loopy belief propagation. Experimental results on the challenging NIST SD27 latent fingerprint database and an overlapped latent fingerprint database demonstrate the advantages of the proposed orientation field estimation algorithm over conventional algorithms.