Análise de Características em Redes Neurais Artificiais Aplicadas em Visão Computacional com Ênfase em Reconhecimento Facial

Com a crescente aplicação da tecnologia de reconhecimento no cotidiano das pessoas, tanto em áreas de autenticação como segurança, técnicas de reconhecimento facial vem sendo estudadas por ser uma das tecnologias menos invasivas e a única que não necessita da cooperação do usuário. Entretanto, por a face ser um elemento altamente deformável ainda são estudadas técnicas mais eficazes de identificar uma face descartando elementos externos como: qualidade da imagem, luminosidade, oclusão e orientação. Desta forma, este artigo explora o resultado de um protótipo de reconhecimento facial baseado em um estudo feito sobre o algoritmo de Viola-Jones e a inclusão de uma rede neural artificial em um hardware integrado, o Raspberry PI

SISTEMA DE CONTROL DE ACCESO USANDO RECONOCIMIENTO FACIAL CON UNA RASPBERRY PI 4 Y OPENCV (ACCESS CONTROL SYSTEM USING FACIAL RECOGNITION WITH A RASPBERRY PI 4 AND OPENCV)

Resumen El objetivo de este trabajo fue realizar un sistema de control de acceso usando reconocimiento facial para acceso a un centro de datos. Se desarrolló usando una tarjeta Raspberry Pi 4, una cámara de video y una pantalla táctil. La programación del sistema implanta el algoritmo de Viola-Jones para la detección del rostro y el reconocimiento del mismo usando funciones de OpenCV. La interfaz de usuario se muestra en la pantalla táctil. Cuando un usuario no autorizado intenta acceder al centro de datos, se transmite un mensaje de alerta de WhatsApp a un teléfono móvil. Las pruebas realizadas mostraron que la exactitud del sistema es 99.6 % y el tiempo de respuesta 400 ns. A partir de los resultados logrados el sistema puede usarse en otro tipo de instalaciones o aplicaciones de tiempo real. Palabras Clave: OpenCV, Raspberry Pi 4, reconocimiento facial, Viola-Jones, WhatsApp. Abstract The objective of this work was to make an access control system using facial recognition for access to a data center. It was developed using a Raspberry Pi 4 card, a video camera and a touch screen. System programming implements the Viola-Jones algorithm for face detection and face recognition using OpenCV functions. The user interface is displayed on the touch screen. When an unauthorized user tries to access the data center, a WhatsApp alert message is transmitted to a mobile phone. The tests carried out showed that the accuracy of the system is 99.6 % and the response time 400 ns. Based on the results achieved, the system can be used in other types of installations or real-time applications. Keywords: Face recognition, OpenCV, Raspberry Pi 4, Viola-Jones, WhatsApp

Learning How To Recognize Faces In Heterogeneous Environments

Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades. Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, such as in border control and in less critical ones, such as to unlock mobile phones. However, the lack of cooperation from the subject and variations on the pose, occlusion and illumination are still open problems and significantly affect error rates. Another challenge that arose recently in face recognition research is the ability of matching faces from different image domains. Use cases encompass the matching between Visual Light images (VIS) with Near infra-red images (NIR), Visual Light images (VIS) with Thermograms or Depth maps. This match can occur even in situations where no real face exists, such as matching using sketches. This task is so called Heterogeneous Face Recognition. The key difficulty in the comparison of faces in heterogeneous conditions is that images from the same subject may differ in appearance due to changes in image domain. In this thesis we address this problem of Heterogeneous Face Recognition (HFR). Our contributions are four-fold. First, we analyze the applicability of crafted features used in face recognition in the HFR task. Second, still working with crafted features, we propose that the variability between two image domains can be suppressed with a linear shift in the Gaussian Mixture Model (GMM) mean subspace. That encompasses inter-session variability (ISV) modeling. Third, we propose that high level features of Deep Convolutional Neural Networks trained on Visual Light images are potentially domain independent and can be used to encode faces sensed in different image domains. Fourth, large-scale experiments are conducted on several HFR databases, covering various image domains showing competitive performances. Moreover, the implementation of all the proposed techniques are integrated into a collaborative open source software library called Bob that enforces fair evaluations and encourages reproducible research