An efficient approach of face detection and recognition from digital images for modern security and office hour attendance system

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.The purpose of this project is to make an efficient security system for university safety measurement which can also be used to calculate the office hours of Student Tutors by face detection and recognition. By using surveillance cameras, attached at all the entrance of university main buildings, the system can detect human faces and then it can recognize people. First, the system captures the image of a person who enters into the building and then detects the face from the image. Then the recognition system matches that image with the given database of images with valid information. After matching that image if the system recognize that face it gives a green signal to allow that person. Otherwise, if the system cannot recognize that face it gives an alert signal to block that person as an intruder. Also, this system calculates the office hours of the Student Tutors. By using face recognition the system takes the starting time and ending time of the Student Tutors individually and then gives the result as output by calculating the time duration

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

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

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Continuous multibiometric authentication for online exam with machine learning

Multibiometric authentication has been received great attention over the past decades with the growing demand of a robust authentication system. Continuous authentication system verifies a user continuously once a person is login in order to prevent intruders from the impersonation. In this study, we propose a continuous multibiometric authentication system for the identification of the person during online exam using two modalities, face recognition and keystrokes. Each modality is separately processed to generate matching scores, and the fusion method is performed at the score level to improve the accuracy. The EigenFace and support vector machine (SVM) approach are applied to the facial recognition and keystrokes dynamic accordingly. The matching score calculated from each modality is combined using the classification by the decision tree with the weighted sum after the score is split into three zones of interes

Automatic behavior recognition in laboratory animals using kinect

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Research method detection human face in video streams

То же на с. 58-6

Authentication of Students and Students’ Work in E-Learning : Report for the Development Bid of Academic Year 2010/11

Global e-learning market is projected to reach $107.3 billion by 2015 according to a new report by The Global Industry Analyst (Analyst 2010). The popularity and growth of the online programmes within the School of Computer Science obviously is in line with this projection. However, also on the rise are students’ dishonesty and cheating in the open and virtual environment of e-learning courses (Shepherd 2008). Institutions offering e-learning programmes are facing the challenges of deterring and detecting these misbehaviours by introducing security mechanisms to the current e-learning platforms. In particular, authenticating that a registered student indeed takes an online assessment, e.g., an exam or a coursework, is essential for the institutions to give the credit to the correct candidate. Authenticating a student is to ensure that a student is indeed who he says he is. Authenticating a student’s work goes one step further to ensure that an authenticated student indeed does the submitted work himself. This report is to investigate and compare current possible techniques and solutions for authenticating distance learning student and/or their work remotely for the elearning programmes. The report also aims to recommend some solutions that fit with UH StudyNet platform.Submitted Versio

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2