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

Anti-Spoof Reliable Biometry of Fingerprints Using En-Face Optical Coherence Tomography

Optical coherence tomography (OCT) is a relatively new imaging technology which can produce high-reso- lution images of three-dimensional structures. OCT has been mainly used for medical applications such as for ophthalmology and dermatology. In this study we demonstrate its capability in providing much more re- liable biometry identification of fingerprints than conventional methods. We prove that OCT can serve se- cure control of genuine fingerprints as it can detect if extra layers are placed above the finger. This can pre- vent with a high probability, intruders to a secure area trying to foul standard systems based on imaging the finger surface. En-Face OCT method is employed and recommended for its capability of providing not only the axial succession of layers in depth, but the en-face image that allows the traditional pattern identification. Another reason for using such OCT technology is that it is compatible with dynamic focus and therefore can provide enhanced transversal resolution and sensitivity. Two En-Face OCT systems are used to evaluate the need for high resolution and conclusions are drawn in terms of the most potential commercial route to ex- ploitation

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

A Universal Latent Fingerprint Enhancer Using Transformers

Forensic science heavily relies on analyzing latent fingerprints, which are crucial for criminal investigations. However, various challenges, such as background noise, overlapping prints, and contamination, make the identification process difficult. Moreover, limited access to real crime scene and laboratory-generated databases hinders the development of efficient recognition algorithms. This study aims to develop a fast method, which we call ULPrint, to enhance various latent fingerprint types, including those obtained from real crime scenes and laboratory-created samples, to boost fingerprint recognition system performance. In closed-set identification accuracy experiments, the enhanced image was able to improve the performance of the MSU-AFIS from 61.56\% to 75.19\% in the NIST SD27 database, from 67.63\% to 77.02\% in the MSP Latent database, and from 46.90\% to 52.12\% in the NIST SD302 database. Our contributions include (1) the development of a two-step latent fingerprint enhancement method that combines Ridge Segmentation with UNet and Mix Visual Transformer (MiT) SegFormer-B5 encoder architecture, (2) the implementation of multiple dilated convolutions in the UNet architecture to capture intricate, non-local patterns better and enhance ridge segmentation, and (3) the guided blending of the predicted ridge mask with the latent fingerprint. This novel approach, ULPrint, streamlines the enhancement process, addressing challenges across diverse latent fingerprint types to improve forensic investigations and criminal justice outcomes

Region-DH: Region-based Deep Hashing for Multi-Instance Aware Image Retrieval

This paper introduces an instance-aware hashing approach Region-DH for large-scale multi-label image retrieval. The accurate object bounds can significantly increase the hashing performance of instance features. We design a unified deep neural network that simultaneously localizes and recognizes objects while learning the hash functions for binary codes. Region-DH focuses on recognizing objects and building compact binary codes that represent more foreground patterns. Region-DH can flexibly be used with existing deep neural networks or more complex object detectors for image hashing. Extensive experiments are performed on benchmark datasets and show the efficacy and robustness of the proposed Region-DH model

Mixing it Up: Developing Expertise in Forensic Fingerprint Examination Using Interleaved Practice

This item is only available electronically.Forensic fingerprint experts have a superior ability to differentiate highly similar print pairs, especially in comparison to novices (those with no experience in the interpretation of fingerprints). Few studies have investigated methods of effectively training novices to become experts. The current study draws on the principle of interleaved practice to train a small sample of fingerprint novices. Interleaving theory purports that ‘mixing’ exemplars from different categories has greater learning benefit than ‘massing’ exemplars from the same category. The current experiment applied this principle via a novel training paradigm in which one group of novices responded to fingerprints from different fingers (Mixed), and a second group responded to fingerprints from the same finger (Massed). An active control group completed a task unrelated to fingerprint examination. All participants completed a measure of fingerprint expertise performance (the xQ) immediately prior to each of 10 training sessions across 10 consecutive days, with a final measure of performance completed on the eleventh and final day of testing. It was predicted that both fingerprint training groups would exhibit significantly greater improvement on the xQ across sessions than controls, and that the Mixed training group would display superior performance across sessions compared to the Massed training group. Instead, the results suggested that, while the Massed training group performed more accurately overall, none of the three groups improved significantly over sessions. This study has potential implications for the training of future fingerprint experts and could reduce the risk of costly errors made by these experts.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 202

Variation in amino acid and lipid composition of latent fingerprints

The enhancement of latent fingerprints, both at the crime scene and in the laboratory using an array of chemical, physical and optical techniques, permits their use for identification. Despite the plethora of techniques available, there are occasions when latent fingerprints are not successfully enhanced. An understanding of latent fingerprint chemistry and behaviour will aid the improvement of current techniques and the development of novel ones. In this study the amino acid and fatty acid content of ‘real’ latent fingerprints collected on a non-porous surface was analysed by gas chromatography–mass spectrometry. Squalene was also quantified in addition. Hexadecanoic acid, octadecanoic acid and cis-9- octadecenoic acid were the most abundant fatty acids in all samples. There was, however, wide variation in the relative amounts of each fatty acid in each sample. It was clearly demonstrated that touching sebum-rich areas of the face immediately prior to fingerprint deposition resulted in a significant increase in the amount of fatty acids and squalene deposited in the resulting ‘groomed’ fingerprints. Serine was the most abundant amino acid identified followed by glycine, alanine and aspartic acid. The significant quantitative differences between the ‘natural’ and ‘groomed’ fingerprint samples seen for fatty acids were not observed in the case of the amino acids. This study demonstrates the variation in latent fingerprint composition between individuals and the impact of the sampling protocol on the quantitative analysis of fingerprints

Interpol review of fingermarks and other body impressions 2016–2019

This review paper covers the forensic-relevant literature in fingerprint and bodily impression sciences from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/ 14458/file/Interpol%20 Review%20 Papers%202019. pdf