Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency

Security and Communication Networks ◽

10.1155/2018/4529652 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Manhua Liu ◽

Shuxin Liu ◽

Weiwu Yan

Keyword(s):

Local Features ◽

Poor Quality ◽

Texture Image ◽

Law Enforcement Agencies ◽

Latent Fingerprints ◽

Latent Fingerprint ◽

Processing Step ◽

Fingerprint Segmentation ◽

Fingerprint Database ◽

Global And Local

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.

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Boosting the classification performance of latent fingerprint segmentation using cascade of classifiers

Intelligent Decision Technologies ◽

10.3233/idt-190105 ◽

2020 ◽

Vol 14 (3) ◽

pp. 359-371 ◽

Cited By ~ 1

Author(s):

Megha Chhabra ◽

Manoj Kumar Shukla ◽

Kiran Kumar Ravulakollu

Keyword(s):

Binary Classification ◽

Poor Quality ◽

Classification Performance ◽

Latent Fingerprints ◽

Fingerprint Segmentation ◽

Binary Classifiers ◽

Maximal Independent Sets ◽

Optimal Set ◽

Optimal Feature

Segmentation and classification of latent fingerprints is a young challenging area of research. Latent fingerprints are unintentional fingermarks. These marks are ridge patterns left at crime scenes, lifted with latent or unclear view of fingermarks, making it difficult to find the guilty party. The segmentation of lifted images of such finger impressions comes with some unique challenges in domain such as poor quality images, incomplete ridge patterns, overlapping prints etc. The classification of poorly acquired data can be improved with image pre-processing, feeding all or optimal set of features extracted to suitable classifiers etc. Our classification system proposes two main steps. First, various effective extracted features are compartmentalised into maximal independent sets with high correlation value, Second, conventional supervised technique based binary classifiers are combined into a cascade/stack of classifiers. These classifiers are fed with all or optimal feature set(s) for binary classification of fingermarks as ridge patterns from non-ridge background. The experimentation shows improvement in accuracy rate on IIIT-D database with supervised algorithms.

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Influences of Global and Local Features on Eye-Movement Patterns in Visual-Similarity Perception of Synthesized Texture Images

Applied Sciences ◽

10.3390/app10165552 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5552 ◽

Cited By ~ 1

Author(s):

Xiaoying Guo ◽

Liang Li ◽

Akira Asano ◽

Chie Muraki Asano

Keyword(s):

Eye Movement ◽

Mathematical Morphology ◽

Movement Patterns ◽

Local Features ◽

Visual Similarity ◽

Local Similarity ◽

Texture Image ◽

Texture Model ◽

Fixation Points ◽

Global And Local

Global and local features are essential for visual-similarity texture perception. Therefore, understanding how people allocate their visual attention when viewing textures with global or local similarity is important. In this work, we investigate the influences of global and local features of a texture on eye-movement patterns and analyze the relationship between eye-movement patterns and visual-similarity selection. First, we synthesized textures by separately controlling global and local textural features through the primitive, grain, and point configuration (PGPC) texture model, a mathematical morphology-based texture model. Second, we conducted an experiment to acquire eye-movement data where participants identified the texture that was highly similar to the standard texture. Experiment data were obtained through an eye-tracker from 60 participants. The collected eye-tracking data were analyzed in terms of three metrics, including total fixation duration in each region of interest (ROI), fixation-point variance in each ROI, and fixation-transfer counts between different ROIs. Analysis results indicated the following. (1) The global and local features of a texture influenced eye-movement patterns. In particular, the texture image that was globally similar to the standard texture contained dispersed fixation points. By contrast, the texture image that was locally similar to the standard texture contained concentrated fixation points. The domination of global and local features influenced the viewers’ similarity choice. (2) The final visual-similarity selection was related to the fixation-transfer count between different ROIs, but not to the fixation time in each ROI. This research also extends the applicability of the mathematical morphology-based texture model to human visual perception.

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Segmentation of Latent Fingerprint using Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9820.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 3777-3780

Keyword(s):

Neural Network ◽

Complex Structure ◽

Crime Scene ◽

Network Approach ◽

Latent Fingerprints ◽

Neural Network Approach ◽

Latent Fingerprint ◽

Low Visibility ◽

Fingerprint Segmentation ◽

Fingerprint Region

Latent fingerprints are the fingerprints that are left by the criminal unintentionally on the surface of the crime scene. The qualities of the latent fingerprints are very poor due to the overlapping patterns and structured noises. Latent fingerprint segmentation is a difficult task due to low visibility, structured noise, and complex structure. In this paper, a fusion of morphological and neural network approach is purposed for latent fingerprint segmentation. This method automatically segments the fingerprints and non-fingerprints patterns without human intervention. The morphological method is used for segmentation of the fingerprint region. Fingerprint region then divides into y*y blocks and extracts the features of each block and uses them as an input of NN to classify the blocks into fingerprint and non-fingerprint blocks. We are using the IIIT-D database and the shows that this model batters then the existing model.

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State-of-the-Art: A Systematic Literature Review on Image Segmentation in Latent Fingerprint Forensics

Recent Patents on Computer Science ◽

10.2174/2213275912666190429153952 ◽

2019 ◽

Vol 12 ◽

Cited By ~ 2

Author(s):

Megha Chhabra ◽

Manoj Kumar Shukla ◽

Kiran Kumar Ravulakollu

Keyword(s):

Image Segmentation ◽

Background Noise ◽

State Of The Art ◽

Recognition Rate ◽

Poor Quality ◽

Latent Fingerprint ◽

In The Beginning ◽

The Comparative Study ◽

Finger Skin

: Latent fingerprints are unintentional finger skin impressions left as ridge patterns at crime scenes. A major challenge in latent fingerprint forensics is the poor quality of the lifted image from the crime scene. Forensics investigators are in permanent search of novel outbreaks of the effective technologies to capture and process low quality image. The accuracy of the results depends upon the quality of the image captured in the beginning, metrics used to assess the quality and thereafter level of enhancement required. The low quality of the image collected by low quality scanners, unstructured background noise, poor ridge quality, overlapping structured noise result in detection of false minutiae and hence reduce the recognition rate. Traditionally, Image segmentation and enhancement is partially done manually using help of highly skilled experts. Using automated systems for this work, differently challenging quality of images can be investigated faster. This survey amplifies the comparative study of various segmentation techniques available for latent fingerprint forensics.

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A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

Applied Sciences ◽

10.3390/app11052174 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2174

Author(s):

Xiaoguang Li ◽

Feifan Yang ◽

Jianglu Huang ◽

Li Zhuo

Keyword(s):

Local Features ◽

Attention Mechanism ◽

Experimental Results ◽

Dynamic Scene ◽

Feature Maps ◽

Training Scheme ◽

Real Scene ◽

Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

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Online multi-object tracking based on global and local features

2016 Visual Communications and Image Processing (VCIP) ◽

10.1109/vcip.2016.7805435 ◽

2016 ◽

Cited By ~ 2

Author(s):

Liang Xu ◽

Weihai Li ◽

Huiling Wu ◽

Qiang Li

Keyword(s):

Object Tracking ◽

Local Features ◽

Global And Local

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Longitudinal study of fingerprint recognition

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1410272112 ◽

2015 ◽

Vol 112 (28) ◽

pp. 8555-8560 ◽

Cited By ~ 56

Author(s):

Soweon Yoon ◽

Anil K. Jain

Keyword(s):

Statistical Models ◽

Recognition Accuracy ◽

Temporal Stability ◽

Poor Quality ◽

Time Span ◽

Fingerprint Recognition ◽

Time Interval ◽

General Belief ◽

Probability Of Error ◽

Fingerprint Database

Human identification by fingerprints is based on the fundamental premise that ridge patterns from distinct fingers are different (uniqueness) and a fingerprint pattern does not change over time (persistence). Although the uniqueness of fingerprints has been investigated by developing statistical models to estimate the probability of error in comparing two random samples of fingerprints, the persistence of fingerprints has remained a general belief based on only a few case studies. In this study, fingerprint match (similarity) scores are analyzed by multilevel statistical models with covariates such as time interval between two fingerprints in comparison, subject’s age, and fingerprint image quality. Longitudinal fingerprint records of 15,597 subjects are sampled from an operational fingerprint database such that each individual has at least five 10-print records over a minimum time span of 5 y. In regard to the persistence of fingerprints, the longitudinal analysis on a single (right index) finger demonstrates that (i) genuine match scores tend to significantly decrease when time interval between two fingerprints in comparison increases, whereas the change in impostor match scores is negligible; and (ii) fingerprint recognition accuracy at operational settings, nevertheless, tends to be stable as the time interval increases up to 12 y, the maximum time span in the dataset. However, the uncertainty of temporal stability of fingerprint recognition accuracy becomes substantially large if either of the two fingerprints being compared is of poor quality. The conclusions drawn from 10-finger fusion analysis coincide with the conclusions from single-finger analysis.

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