A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research

Image enhancement and segmentation on simultaneous latent fingerprint detection

A simultaneous latent fingerprint (SLF) image consists of multi-print of individual fingerprints that is lifted from a surface, typically at the crime scenes. Due to the nature and the poor quality of latent fingerprint image, segmentation becomes an important and very challenging task. This thesis presents an algorithm to segment individual fingerprints for SLF image. The algorithm aim to separate the fingerprint region of interest from image background, which identifies the distal phalanx portion of each finger that appears in SLF image. The algorithm utilizes ridge orientation and frequency features based on block-wise pixels. A combination of Gabor Filter and Fourier transform is implemented in the normalization stage. In the pre-processing stage, a modified version of Histogram equalization is proposed known as Alteration Histogram Equalization (AltHE). Sliding windows are applied to create bounding boxes in order to find out the distal phalanges region at the segmentation stage. To verify the capability of the proposed segmentation algorithm, the segmentation results is evaluated in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. The ground truth foreground refers to the manual mark up region of interest area. In order to evaluate the performance of this method, experiments are performed on the Indian Institute of Information Technology Database- Simultaneous Latent Fingerprint (IIITD-SLF). Using the proposed algorithm, the segmented images were supplied as the input image for the matching process via a state art of matcher, VeriFinger SDK. Segmentation of 240 images is performed and compared with manual segmentation methods. The results show that the proposed algorithm achieves a correct segmentation of 77.5% of the SLF images under test

An Algorithm for Fingerprint Classification Using Template Matching Technique

Automatic fingerprint classification has received considerable attention over the past decade. Despite significant progress in this field, there are still rooms for improving the classification operation by continuing study and research in this field. This thesis describes a study of fingerprint classification using template matching technique. We have classified the fingerprints in four groups according to their pattern, which are Arch, Left loop, Right loop, and Whorl. We have discussed and explained the specification and the limitations of the fingerprint classification (the effect of corrupted and rotated input fingerprints on the accuracy of the classification operation). The thesis has analysed the mentioned technique and evaluated its strengths and limitation by comparing this technique with the singularities technique. This research has also included the pre-processing stage, which consist of enhancement, segmentation, and thinning of fingerprints

A Longitudinal Analysis on the Feasibility of Iris Recognition Performance for Infants 0-2 Years Old

The focus of this study was to longitudinally evaluate iris recognition for infants between the ages of 0 to 2 years old. Image quality metrics of infant and adult irises acquired on the same iris camera were compared. Matching performance was evaluated for four groups, infants 0 to 6 months, 7 to 12 months, 13 to 24 months, and adults. A mixed linear regression model was used to determine if infants’ genuine similarity scores changed over time. This study found that image quality metrics were different between infants and adults but in the older group, (13 to 24 months old) the image quality metric scores were more likely to be similar to adults. Infants 0 to 6 months old had worse performance at an FMR of 0.01% than infants 7 to 12 months, 13 to 24 months, and adults

FLAG : the fault-line analytic graph and fingerprint classification

Fingerprints can be classified into millions of groups by quantitative measurements of their new representations - Fault-Line Analytic Graphs (FLAG), which describe the relationship between ridge flows and singular points. This new model is highly mathematical, therefore, human interpretation can be reduced to a minimum and the time of identification can be significantly reduced. There are some well known features on fingerprints such as singular points, cores and deltas, which are global features which characterize the fingerprint pattern class, and minutiae which are the local features which characterize an individual fingerprint image. Singular points are more important than minutiae when classifying fingerprints because the geometric relationship among the singular points decide the type of fingerprints. When the number of fingerprint records becomes large, the current methods need to compare a large number of fingerprint candidates to identify a given fingerprint. This is the result of having a few synthetic types to classify a database with millions of fingerprints. It has been difficult to enlarge the minter of classification groups because there was no computational method to systematically describe the geometric relationship among singular points and ridge flows. In order to define a more efficient classification method, this dissertation also provides a systematic approach to detect singular points with almost pinpoint precision of 2x2 pixels using efficient algorithms