Anti-spoofing using challenge-response user interaction

2D facial identification has attracted a great amount of attention over the past years, due to its several advantages including practicality and simple requirements. However, without its capability to recognize a real user from an impersonator, face identification system becomes ineffective and vulnerable to spoof attacks. With the great evolution of smart portable devices, more advanced sorts of attacks have been developed, especially the replayed videos spoofing attempts that are becoming more difficult to recognize. Consequently, several studies have investigated the types of vulnerabilities a face biometric system might encounter and proposed various successful anti-spoofing algorithms. Unlike spoofing detection for passive or motionless authentication methods that were profoundly studied, anti-spoofing systems applied on interactive user verification methods were broadly examined as a potential robust spoofing prevention approach. This study aims first at comparing the performance of the existing spoofing detection techniques on passive and interactive authentication methods using a more balanced collected dataset and second proposes a fusion scheme that combines both texture analysis with interaction in order to enhance the accuracy of spoofing detection

Biologically Inspired Processing for Lighting Robust Face Recognition

ISBN 978-953-307-489-4, Hard cover, 314 pagesNo abstrac

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Low-cost face biometry for visually impaired users

We present a work in progress on a face biometry system for visually impaired users - the result of a very close interaction among scientists, engineers, and a users group formed by visually impaired and social assistants. The prototype under development implements recent trends of video analysis and follows closely the suggestions given by the working group, with the ambitious goal of developing a device easy to use that can be an effective help to improve communication and inclusion of visually impaired population. The prototype works real-time processing the incoming video stream to the purpose of locating the presence of people and spotting known faces. Each event of interest produces a simple audio feedback to the user, allowing him or her to locate the presence of people before they start talking or highlighting known faces in noisy environments. So far the prototype has been quantitatively validated through a set of experiments carried out in lab; also a qualitative evaluation by a heterogeneous group of perspective users, users' relatives, social workers, and experts gave us many positive comments and useful feedbacks for future work

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Multi-modal Active Authentication of Smartphone Users

With the increasing usage of smartphones not only as communication devices but also as the port of entry for a wide variety of user accounts at different information sensitivity levels, the need for hassle-free authentication is on the rise. Going beyond the traditional one-time authentication concept, active authentication (AA) schemes are emerging which authenticates users periodically in the background without the need for any user interaction. The purpose of this research is to explore different aspects of the AA problem and develop viable solutions by extracting unique biometric traits of the user from the wide variety of usage data obtained from Smartphone sensors. The key aspects of our research are the development of different components of user verification algorithms based on (a) face images from the front camera and (b) data from modalities other than the face. Since generic face detection algorithms do not perform very well in the mobile domain due to a significant presence of occluded and partially visible faces, we propose facial segment-based face detection technique to handle the challenge of partial faces in the mobile domain. We have developed three increasingly accurate proposal-based face detection methods, namely Facial Segment-based Face Detector (FSFD), SegFace and DeepSegFace, respectively, which perform binary classification on the results of a novel proposal generator that utilizes facial segments to obtain face-proposals. We also propose the Deep Regression-based User Image Detector (DRUID) network which shifts from the classification to the regression paradigm to avoid the need for proposal generation and thereby, achieves better processing speed and accuracy. DeepSegFace and DRUID have unique network architectures with customized loss functions and utilize a novel data augmentation scheme to train on a relatively small amount of data. The proposed methods, especially DRUID show superior performance over other state-of-the-art face detectors in terms of precision-recall and ROC curve on two mobile face datasets. We extended the concept of facial-segments to facial attribute detection for partially visible faces, a topic rarely addressed in the literature. We developed a deep convolutional neural network-based method named Segment-wise, Partial, Localized Inference in Training Facial Attribute Classification Ensembles (SPLITFACE) to detect attributes reliably from partially occluded faces. Taking several facial segments and the full face as input, SPLITFACE takes a data-driven approach to determine which attributes are localized in which facial segments. The unique architecture of the network allows each attribute to be predicted by multiple segments, which permits the implementation of committee machine techniques for combining local and global decisions to boost performance. Our evaluations on the full CelebA and LFWA datasets and their modified partial-visibility versions show that SPLITFACE significantly outperforms other recent attribute detection methods, especially for partial faces and for cross-domain experiments. We also explored the potentials of two less popular modalities namely, location history and application-usage, for active authentication. Aiming to discover the pattern of life of a user, we processed the location traces into separate state space models for each user and developed the Marginally Smoothed Hidden Markov Model (MSHMM) algorithm to authenticate the current user based on the most recent sequence of observations. The method takes into consideration the sparsity of the available data, the transition phases between states, the timing information and also the unforeseen states. We looked deeper into the impact of unforeseen and unknown states in another research work where we evaluated the feasibility of application usage behavior of the users as a potential solution to the active authentication problem. Our experiments show that it is essential to take unforeseen states into account when designing an authentication system with sparse data and marginal-smoothing techniques are very useful in this regard. We conclude this dissertation with the description of some ongoing efforts and future directions of research related the topics discussed in addition to a summary of all the contributions and impacts of this research work

Verificação facial em duas etapas para dispositivos móveis

Orientadores: Jacques Wainer, Fernanda Alcântara AndalóDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Dispositivos móveis, como smartphones e tablets, se tornaram mais populares e acessíveis nos últimos anos. Como consequência de sua ubiquidade, esses aparelhos guardam diversos tipos de informações pessoais (fotos, conversas de texto, coordenadas GPS, dados bancários, entre outros) que só devem ser acessadas pelo dono do dispositivo. Apesar de métodos baseados em conhecimento, como senhas numéricas ou padrões, ainda estejam entre as principais formas de assegurar a identidade do usuário, traços biométricos tem sido utilizados para garantir uma autenticação mais segura e prática. Entre eles, reconhecimento facial ganhou atenção nos últimos anos devido aos recentes avanços nos dispositivos de captura de imagens e na crescente disponibilidade de fotos em redes sociais. Aliado a isso, o aumento de recursos computacionais, com múltiplas CPUs e GPUs, permitiu o desenvolvimento de modelos mais complexos e robustos, como redes neurais profundas. Porém, apesar da evolução das capacidades de dispositivos móveis, os métodos de reconhecimento facial atuais ainda não são desenvolvidos considerando as características do ambiente móvel, como processamento limitado, conectividade instável e consumo de bateria. Neste trabalho, nós propomos um método de verificação facial otimizado para o ambiente móvel. Ele consiste em um procedimento em dois níveis que combina engenharia de características (histograma de gradientes orientados e análise de componentes principais por regiões) e uma rede neural convolucional para verificar se o indivíduo presente em uma imagem corresponde ao dono do dispositivo. Nós também propomos a \emph{Hybrid-Fire Convolutional Neural Network}, uma arquitetura ajustada para dispositivos móveis que processa informação de pares de imagens. Finalmente, nós apresentamos uma técnica para adaptar o limiar de aceitação do método proposto para imagens com características diferentes daquelas presentes no treinamento, utilizando a galeria de imagens do dono do dispositivo. A solução proposta se compara em acurácia aos métodos de reconhecimento facial do estado da arte, além de possuir um modelo 16 vezes menor e 4 vezes mais rápido ao processar uma imagem em smartphones modernos. Por último, nós também organizamos uma base de dados composta por 2873 selfies de 56 identidades capturadas em condições diversas, a qual esperamos que ajude pesquisas futuras realizadas neste cenárioAbstract: Mobile devices, such as smartphones and tablets, had their popularity and affordability greatly increased in recent years. As a consequence of their ubiquity, these devices now carry all sorts of personal data (\emph{e.g.} photos, text conversations, GPS coordinates, banking information) that should be accessed only by the device's owner. Even though knowledge-based procedures, such as entering a PIN or drawing a pattern, are still the main methods to secure the owner's identity, recently biometric traits have been employed for a more secure and effortless authentication. Among them, face recognition has gained more attention in past years due to recent improvements in image-capturing devices and the availability of images in social networks. In addition to that, the increase in computational resources, with multiple CPUs and GPUs, enabled the design of more complex and robust models, such as deep neural networks. Although the capabilities of mobile devices have been growing in past years, most recent face recognition techniques are still not designed considering the mobile environment's characteristics, such as limited processing power, unstable connectivity and battery consumption. In this work, we propose a facial verification method optimized to the mobile environment. It consists of a two-tiered procedure that combines hand-crafted features (histogram of oriented gradients and local region principal component analysis) and a convolutional neural network to verify if the person depicted in a picture corresponds to the device owner. We also propose \emph{Hybrid-Fire Convolutional Neural Network}, an architecture tweaked for mobile devices that process encoded information of a pair of face images. Finally, we expose a technique to adapt our method's acceptance thresholds to images with different characteristics than those present during training, by using the device owner's enrolled gallery. The proposed solution performs a par to the state-of-the-art face recognition methods, while having a model 16 times smaller and 4 times faster when processing an image in recent smartphone models. Finally, we have collected a new dataset of selfie pictures comprising 2873 images from 56 identities with varied capture conditions, that hopefully will support future researches in this scenarioMestradoCiência da ComputaçãoMestre em Ciência da Computaçã