Contributions in face detection with deep neural networks

• Ano de defesa: 2017
• Autor(a) principal: Paula, Thomas da Silva
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul; Faculdade de Informática; Brasil; PUCRS; Programa de Pós-Graduação em Ciência da Computação
• Palavras-chave em Português: Deep Learning; Face Detection; Neural Networks; Machine Learning; Computer Vision; Aprendizado Profundo; Reconhecimento Facial; Redes Neurais; Aprendizado de Máquina; Visão Computacional; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/7563

Face Detection is one of the most studied subjects in the Computer Vision field. Given an arbitrary image or video frame, the goal of face detection is to determine whether there are any faces in the image and, if present, return the image location and the extent of each face. Such a detection is easily done by humans, but it is still a challenge within Computer Vision. The high degree of variability and the dynamicity of the human face makes it an object very difficult to detect, mainly in complex environments. Recently, Deep Learning approaches started to be applied for Computer Vision tasks with great results. They opened new research possibilities in different applications, including Face Detection. Even though Deep Learning has been successfully applied for such a task, most of the state-of-the-art implementations make use of off-the-shelf face detectors and do not evaluate differences among them. In other cases, the face detectors are trained in a multitask manner that includes face landmark detection, age detection, and so on. Hence, our goal is threefold. First, we summarize and explain many advances of deep learning, detailing how each different architecture and implementation work. Second, we focus on the face detection problem itself, performing a rigorous analysis of some of the existing face detectors as well as implementations of our own. We experiment and evaluate variations of hyper-parameters for each of the detectors and their impact in different datasets. We explore both traditional and more recent approaches, as well as implementing our own face detectors. Finally, we implement, test, and compare a meta learning approach for face detection, which aims to learn the best face detector for a given image. Our experiments contribute in understanding the role of deep learning in face detection as well as the subtleties of changing hyper-parameters of the face detectors and their impact in face detection. We also show how well features obtained with deep neural networks trained on a general-purpose dataset perform on a meta learning approach for face detection. Our experiments and conclusions show that deep learning has indeed a notable role in face detection.