Improvement of fingerprint retrieval by a statistical classifier

The topics of fingerprint classification, indexing, and retrieval have been studied extensively in the past decades. One problem faced by researchers is that in all publicly available fingerprint databases, only a few fingerprint samples from each individual are available for training and testing, making it inappropriate to use sophisticated statistical methods for recognition. Hence most of the previous works resorted to simple $k$-nearest neighbor ($k$-NN) classification. However, the $k$-NN classifier has the drawbacks of being comparatively slow and less accurate. In this paper, we tackle this problem by first artificially expanding the set of training samples using our previously proposed spatial modeling technique. With the expanded training set, we are then able to employ a more sophisticated classifier such as the Bayes classifier for recognition. We apply the proposed method to the problem of one-to-$N$ fingerprint identification and retrieval. The accuracy and speed are evaluated using the benchmarking FVC 2000, FVC 2002, and NIST-4 databases, and satisfactory retrieval performance is achieved. © 2010 IEEE.published_or_final_versio

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Enhanced Stegano-Cryptographic Model for Secure Electronic Voting

The issue of security in Information and Communication Technology has been identified as the most critical barrier in the widespread adoption of electronic voting (e-voting). Earlier cryptographic models for secure e-voting are vulnerable to attacks and existing stegano-cryptographic models can be manipulated by an eavesdropper. These shortcomings of existing models of secure e-voting are threats to confidentiality, integrity and verifiability of electronic ballot which are critical to overall success of e-democratic decision making through e-voting.This paper develops an enhanced stegano-cryptographic model for secure electronic voting system in poll-site, web and mobile voting scenarios for better citizens’ participation and credible e-democratic election. The electronic ballot was encrypted using Elliptic Curve Cryptography and Rivest-Sharma-Adleman cryptographic algorithm. The encrypted voter’s ballot was scattered and hidden in the Least Significant Bit (LSB) of the cover media using information hiding attribute of modified LSB-Wavelet steganographic algorithm. The image quality of the model, stego object was quantitatively assessed using Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE) and Structural Similarity Index Metrics (SSIM).The results after quantitative performance evaluation shows that the developed stegano-cryptographic model has generic attribute of secured e-voting relevant for the delivery of credible e-democratic decision making. The large scale implementation of the model would be useful to deliver e-voting of high electoral integrity and political trustworthiness, where genuine e-elections are conducted for the populace by government authority. Keywords: Electronic Voting, Cryptography, Steganography, Video, Image, Wavelet, Securit

Multimodal microscopy in mid-infrared via flexible pulse shaping

In this thesis, mid-infrared (MIR) pulses with arbitrary temporal and spectral shape are generated via a difference-frequency process for application in a non-linear Raman microscope. Solely by shaping the sub 10 fs driving pulses, the broadband spectra of the MIR pulses are switched to narrowband and tuneable ones. In MIR transmission spectroscopy, these narrowband MIR spectra allow for investigating molecular vibrations from 1250 to 3250 cm-1 with spectral resolutions below 20 cm-1. Furthermore, MIR transmission microspectroscopy is combined with coherent-anti-Stokes Raman scattering (CARS) to provide a direct comparison of spectra and images obtained in one spot of the sample. Sum-frequency (SF) microspectroscopy is an additional technique, which complements the toolbox of this non-linear Raman microscope with the potential to investigate non-centrosymmetric systems. The flexibility of the pulse shaper allows for implementing two different SF-methods. Whereas the heterodyne multiplex method acquires the whole SF spectrum by imprinting only three different phase functions, the homodyne MIR-scanning method generates a high SF intensity directly linked to one vibrational mode. In all applications, the phase of MIR pulses must be well-known. This phase is determined in the focal plane of the microscope over more than 1000 cm-1 via two methods based on the dispersion-scan

An Analysis on Adversarial Machine Learning: Methods and Applications

Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to provide an analysis on these two sides of adversarial learning and harness their potential for improving the robustness and generalization of deep models. In the first part of the dissertation, we study the adversarial susceptibility of deep learning models. We provide an empirical analysis on the extent of vulnerability by proposing two adversarial attacks that explore the geometric and frequency-domain characteristics of inputs to manipulate deep decisions. Afterward, we formalize the susceptibility of deep networks using the first-order approximation of the predictions and extend the theory to the ensemble classification scheme. Inspired by theoretical findings, we formalize a reliable and practical defense against adversarial examples to robustify ensembles. We extend this part by investigating the shortcomings of \gls{at} and highlight that the popular momentum stochastic gradient descent, developed essentially for natural training, is not proper for optimization in adversarial training since it is not designed to be robust against the chaotic behavior of gradients in this setup. Motivated by these observations, we develop an optimization method that is more suitable for adversarial training. In the second part of the dissertation, we harness adversarial learning to enhance the generalization and performance of deep networks in discriminative and generative tasks. We develop several models for biometric identification including fingerprint distortion rectification and latent fingerprint reconstruction. In particular, we develop a ridge reconstruction model based on generative adversarial networks that estimates the missing ridge information in latent fingerprints. We introduce a novel modification that enables the generator network to preserve the ID information during the reconstruction process. To address the scarcity of data, {\it e.g.}, in latent fingerprint analysis, we develop a supervised augmentation technique that combines input examples based on their salient regions. Our findings advocate that adversarial learning improves the performance and reliability of deep networks in a wide range of applications

An evaluation of the mechanisms of recovery of DNA and fingerprints from fire scenes

Incidents involving the intentional or deliberate setting of a fire within a compartment are frequently difficult to investigate both because of the damage to the property in question and the apparent lack of forensic evidence which could be used to potentially identify a suspect. The recovery of such evidence in the form of DNA and fingerprints from a fire scene would therefore be advantageous. During this project, replicate samples of DNA and fingerprints were deposited on both porous and non porous surfaces which were then exposed to laboratory controlled elevated temperatures for various time periods. In each case replicate DNA samples or replicate depleted series of fingerprint samples were used to produce robust data sets for subsequent statistical analysis. DNA and fingerprint samples were also exposed to a real fire environment using a fire training facility in order to simulate operational conditions. The results obtained suggest that the optimum recovery method for low template DNA was to use a wet followed by a dry cotton swabbing action of the surface before combining the two swabs for extraction. When the DNA was exposed to elevated temperatures in a controlled environment, there was a greater possibility of recovering a full SGM Plus profile if the DNA had been absorbed into a porous rather than non porous surface and the surface exposed up to a maximum of 100˚C only. All of the samples which were exposed to the uncontrollable fire environment produced partial DNA profiles. The survivability and chemical enhancement of fingerprints deposited on both porous and non porous surfaces was robustly investigated where 70 replicate fingerprints were examined in each case for each test condition. For porous surfaces the most efficient sequence of enhancement techniques was an initial visual examination, followed by a fluorescence examination prior to treatment with DFO, and finally PD. It was found that this sequence could be employed for both wet and dry articles. In the case of dry, non porous surfaces, visual examination followed by fluorescence examination should be utilised prior to undertaking superglue - BY40 treatment. Powder suspension should be substituted for superglue in the case of wet items.Incidents involving the intentional or deliberate setting of a fire within a compartment are frequently difficult to investigate both because of the damage to the property in question and the apparent lack of forensic evidence which could be used to potentially identify a suspect. The recovery of such evidence in the form of DNA and fingerprints from a fire scene would therefore be advantageous. During this project, replicate samples of DNA and fingerprints were deposited on both porous and non porous surfaces which were then exposed to laboratory controlled elevated temperatures for various time periods. In each case replicate DNA samples or replicate depleted series of fingerprint samples were used to produce robust data sets for subsequent statistical analysis. DNA and fingerprint samples were also exposed to a real fire environment using a fire training facility in order to simulate operational conditions. The results obtained suggest that the optimum recovery method for low template DNA was to use a wet followed by a dry cotton swabbing action of the surface before combining the two swabs for extraction. When the DNA was exposed to elevated temperatures in a controlled environment, there was a greater possibility of recovering a full SGM Plus profile if the DNA had been absorbed into a porous rather than non porous surface and the surface exposed up to a maximum of 100˚C only. All of the samples which were exposed to the uncontrollable fire environment produced partial DNA profiles. The survivability and chemical enhancement of fingerprints deposited on both porous and non porous surfaces was robustly investigated where 70 replicate fingerprints were examined in each case for each test condition. For porous surfaces the most efficient sequence of enhancement techniques was an initial visual examination, followed by a fluorescence examination prior to treatment with DFO, and finally PD. It was found that this sequence could be employed for both wet and dry articles. In the case of dry, non porous surfaces, visual examination followed by fluorescence examination should be utilised prior to undertaking superglue - BY40 treatment. Powder suspension should be substituted for superglue in the case of wet items

Artificial Intelligence in the Creative Industries: A Review

This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the `creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -- where it is designed to augment, rather than replace, human creativity