ID Photograph hashing : a global approach

This thesis addresses the question of the authenticity of identity photographs, part of the documents required in controlled access. Since sophisticated means of reproduction are publicly available, new methods / techniques should prevent tampering and unauthorized reproduction of the photograph. This thesis proposes a hashing method for the authentication of the identity photographs, robust to print-and-scan. This study focuses also on the effects of digitization at hash level. The developed algorithm performs a dimension reduction, based on independent component analysis (ICA). In the learning stage, the subspace projection is obtained by applying ICA and then reduced according to an original entropic selection strategy. In the extraction stage, the coefficients obtained after projecting the identity image on the subspace are quantified and binarized to obtain the hash value. The study reveals the effects of the scanning noise on the hash values of the identity photographs and shows that the proposed method is robust to the print-and-scan attack. The approach focusing on robust hashing of a restricted class of images (identity) differs from classical approaches that address any imageCette thèse traite de la question de l’authenticité des photographies d’identité, partie intégrante des documents nécessaires lors d’un contrôle d’accès. Alors que les moyens de reproduction sophistiqués sont accessibles au grand public, de nouvelles méthodes / techniques doivent empêcher toute falsification / reproduction non autorisée de la photographie d’identité. Cette thèse propose une méthode de hachage pour l’authentification de photographies d’identité, robuste à l’impression-lecture. Ce travail met ainsi l’accent sur les effets de la numérisation au niveau de hachage. L’algorithme mis au point procède à une réduction de dimension, basée sur l’analyse en composantes indépendantes (ICA). Dans la phase d’apprentissage, le sous-espace de projection est obtenu en appliquant l’ICA puis réduit selon une stratégie de sélection entropique originale. Dans l’étape d’extraction, les coefficients obtenus après projection de l’image d’identité sur le sous-espace sont quantifiés et binarisés pour obtenir la valeur de hachage. L’étude révèle les effets du bruit de balayage intervenant lors de la numérisation des photographies d’identité sur les valeurs de hachage et montre que la méthode proposée est robuste à l’attaque d’impression-lecture. L’approche suivie en se focalisant sur le hachage robuste d’une classe restreinte d’images (d’identité) se distingue des approches classiques qui adressent une image quelconqu

Perceptually inspired image estimation and enhancement

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.Includes bibliographical references (p. 137-144).In this thesis, we present three image estimation and enhancement algorithms inspired by human vision. In the first part of the thesis, we propose an algorithm for mapping one image to another based on the statistics of a training set. Many vision problems can be cast as image mapping problems, such as, estimating reflectance from luminance, estimating shape from shading, separating signal and noise, etc. Such problems are typically under-constrained, and yet humans are remarkably good at solving them. Classic computational theories about the ability of the human visual system to solve such under-constrained problems attribute this feat to the use of some intuitive regularities of the world, e.g., surfaces tend to be piecewise constant. In recent years, there has been considerable interest in deriving more sophisticated statistical constraints from natural images, but because of the high-dimensional nature of images, representing and utilizing the learned models remains a challenge. Our techniques produce models that are very easy to store and to query. We show these techniques to be effective for a number of applications: removing noise from images, estimating a sharp image from a blurry one, decomposing an image into reflectance and illumination, and interpreting lightness illusions. In the second part of the thesis, we present an algorithm for compressing the dynamic range of an image while retaining important visual detail. The human visual system confronts a serious challenge with dynamic range, in that the physical world has an extremely high dynamic range, while neurons have low dynamic ranges.(cont.) The human visual system performs dynamic range compression by applying automatic gain control, in both the retina and the visual cortex. Taking inspiration from that, we designed techniques that involve multi-scale subband transforms and smooth gain control on subband coefficients, and resemble the contrast gain control mechanism in the visual cortex. We show our techniques to be successful in producing dynamic-range-compressed images without compromising the visibility of detail or introducing artifacts. We also show that the techniques can be adapted for the related problem of "companding", in which a high dynamic range image is converted to a low dynamic range image and saved using fewer bits, and later expanded back to high dynamic range with minimal loss of visual quality. In the third part of the thesis, we propose a technique that enables a user to easily localize image and video editing by drawing a small number of rough scribbles. Image segmentation, usually treated as an unsupervised clustering problem, is extremely difficult to solve. With a minimal degree of user supervision, however, we are able to generate selection masks with good quality. Our technique learns a classifier using the user-scribbled pixels as training examples, and uses the classifier to classify the rest of the pixels into distinct classes. It then uses the classification results as per-pixel data terms, combines them with a smoothness term that respects color discontinuities, and generates better results than state-of-art algorithms for interactive segmentation.by Yuanzhen Li.Ph.D

Mixing Biometric Data For Generating Joint Identities and Preserving Privacy

Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems

Digital imaging technology assessment: Digital document storage project

An ongoing technical assessment and requirements definition project is examining the potential role of digital imaging technology at NASA's STI facility. The focus is on the basic components of imaging technology in today's marketplace as well as the components anticipated in the near future. Presented is a requirement specification for a prototype project, an initial examination of current image processing at the STI facility, and an initial summary of image processing projects at other sites. Operational imaging systems incorporate scanners, optical storage, high resolution monitors, processing nodes, magnetic storage, jukeboxes, specialized boards, optical character recognition gear, pixel addressable printers, communications, and complex software processes

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp