Progressive-Regressive Strategy for Biometrical Authentication

This chapter thoroughly investigates the use of the progressive–regressive strategy for biometrical authentication through the use of human gait and face images. A considerable amount of features were extracted and relevant parameters computed for such an investigation and a vast number of datasets developed. The datasets consist of features and computed parameters extracted from human gait and face images from various subjects of different ages. Soft-computing techniques, discrete wavelet transform (DWT), principal component analysis and the forward–backward dynamic programming method were applied for the best-fit selection of parameters and the complete matching process. The paretic and non-paretic characteristics were classified through Naïve Bayes’ classification theorem. Both classification and recognition were carried out in parallel with test and trained datasets and the whole process of investigation was successfully carried out through an algorithm developed in this chapter. The success rate of biometrical authentication is 89%

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Visual Computing and Machine Learning Techniques for Digital Forensics

It is impressive how fast science has improved day by day in so many different fields. In special, technology advances are shocking so many people bringing to their reality facts that previously were beyond their imagination. Inspired by methods earlier presented in scientific fiction shows, the computer science community has created a new research area named Digital Forensics, which aims at developing and deploying methods for fighting against digital crimes such as digital image forgery.This work presents some of the main concepts associated with Digital Forensics and, complementarily, presents some recent and powerful techniques relying on Computer Graphics, Image Processing, Computer Vision and Machine Learning concepts for detecting forgeries in photographs. Some topics addressed in this work include: sourceattribution, spoofing detection, pornography detection, multimedia phylogeny, and forgery detection. Finally, this work highlights the challenges and open problems in Digital Image Forensics to provide the readers with the myriad opportunities available for research

Risk-Adapted Access Control with Multimodal Biometric Identification

The presented article examines the background of biometric identification. As a technical method of authentication, biometrics suffers from some limitations. These limitations are due to human nature, because skin, appearance and behavior changes more or less continuously in time. Changing patterns affect quality and always pose a significantly higher risk. This study investigated risk adaption and the integration of the mathematical representation of this risk into the whole authentication process. Several biometrical identification methods have been compared in order to find an algorithm of a multimodal biometric identification process as a possible solution to simultaneously improve the rates of failed acceptations and rejections. This unique solution is based on the Adaptive Neuro-Fuzzy Inference System and the Bayesian Theorem

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

A Survey on Soft Biometrics for Human Identification

The focus has been changed to multi-biometrics due to the security demands. The ancillary information extracted from primary biometric (face and body) traits such as facial measurements, gender, color of the skin, ethnicity, and height is called soft biometrics and can be integrated to improve the speed and overall system performance of a primary biometric system (e.g., fuse face with facial marks) or to generate human semantic interpretation description (qualitative) of a person and limit the search in the whole dataset when using gender and ethnicity (e.g., old African male with blue eyes) in a fusion framework. This chapter provides a holistic survey on soft biometrics that show major works while focusing on facial soft biometrics and discusses some of the features of extraction and classification techniques that have been proposed and show their strengths and limitations

THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system

In this paper, we propose a new biometric verification and template protection system which we call the THRIVE system. The system includes novel enrollment and authentication protocols based on threshold homomorphic cryptosystem where the private key is shared between a user and the verifier. In the THRIVE system, only encrypted binary biometric templates are stored in the database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during the authentication stage. The THRIVE system is designed for the malicious model where the cheating party may arbitrarily deviate from the protocol specification. Since threshold homomorphic encryption scheme is used, a malicious database owner cannot perform decryption on encrypted templates of the users in the database. Therefore, security of the THRIVE system is enhanced using a two-factor authentication scheme involving the user's private key and the biometric data. We prove security and privacy preservation capability of the proposed system in the simulation-based model with no assumption. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form but she needs to proof her physical presence by using biometrics. The system can be used with any biometric modality and biometric feature extraction scheme whose output templates can be binarized. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biohash vectors on a desktop PC running with quad-core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real life applications

A high performance biometric system based on image morphological analysis

At present, many of the algorithms used and proposed for digital imaging biometric systems are based on mathematical complex models, and this fact is directly related to the performance of any computer implementation of these algorithms. On the other hand, as they are conceived for general purpose digital imaging, these algorithms do not take advantage of any common morphological features from its given domains. In this paper we developed a novel algorithm for the segmentation of the pupil and iris in human eye images, whose improvement’s hope lies in the use of morphological features of the images of the human eye. Based on the basic structure of a standard biometric system we developed and implemented an innovation for each phase of the system, avoiding the use of mathematical complex models and exploiting some common features in any digital image of the human eye from the dataset that we used. Finally, we compared the testing results against other known state of the art works developed over the same dataset.publishedVersionFil: Rocchietti, Marco Augusto. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Scerbo, Alejandro Luis Ángel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Ojeda, Silvia María. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Ciencias de la Computació

Kompjutersko pretraživanje i upoređivanje podataka - opšta razmatranja i primena u kriminalistici

Collecting the most versatile kind of information about the citizens and their storing in the appropriate bases represent the reality of the contemporary society. The growth in the quantity of these pieces of information has exceeded human power to process and analyze such huge quantities of data in a traditional manner, requiring computerized techniques and means for these needs. Although widely applied for years in the work of public administration and economy, so far the computer data search and comparison have not been sufficiently used in crime investigation and forensics. Police agencies and forensic laboratories collect large quantities of various data which originate as a result of processing numerous criminal activities. The very success of their automatic search and comparison within criminal investigations depends to a large extent on the availability and characteristics of data (features, raster) which refer to persons, objects or events.Prikupljanje odgovarajućih informacija o građanima iz najrazličitijih (naravno legalnih i legitimnih) motiva i u najrazličitije svrhe, te njihovo smeštanje u odgovarajuće baze, predstavlja realnost savremenog društva. Razvoj računarske tehnologije u velikoj meri je povećao mogućnosti prijema, obrade i praćenja takvih podataka, pa čak i u svrhe nadzora nad pojedincem i njegovim ponašanjem. Automatsko pretraživanje i upoređivanje podataka, nezavisno od toga u koje se svrhe primenjuje, zasniva se sa jedne strane na bazama u kojima su smešteni određeni podaci, i, sa druge strane, primeni računara (shvaćenog kao hardver) i odgovarajućih programa (softver) kojima se ti podaci pretražuju, upoređuju i analiziraju. Kompjutersko pretraživanje, analiziranje i upoređivanje podataka u kriminalističke svrhe može biti veoma raznovrsno, sa različitim očekivanjima i rezultatima primene. Policijske agencije i forenzičke laboratorije sakupljaju velike količine različitih podatka, koji nastaju kao rezultat obrade brojnih kriminalnih aktivnosti. Veliki izazov sa kojim se suočavaju sve policijske i obaveštajne agencije jeste tačno i efikasno analiziranje podataka o kriminalu, čiji se obim neprestano povećava. Može se reći da su tehnike automatskog pretraživanja i upoređivanja podataka do sada nedovoljno eksploatisane u ovoj oblasti, iako bi mogle dati značajan doprinos. Automatsko pretraživanje i upoređivanje podataka je moćna alatka koja istražiteljima krivičnih dela omogućava brzo i efikasno pretraživanje velikih baza podataka. Posebno razumevanje odnosa između mogućnosti analize i karakteristika određene vrste krivičnog dela može da pomogne istražiteljima da efikasnije primene ove tehnike kako bi identifikovali trendove i obrasce, locirali problematična područja, pa čak i predvideli krivično delo