@MASTERSTHESIS{ 2017:591521646,
 	title = {Contributions in face detection with deep neural networks},
 	year = {2017},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/7563",
 	abstract = "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.",
 	publisher = {Pontifícia Universidade Católica do Rio Grande do Sul},
 	scholl = {Programa de Pós-Graduação em Ciência da Computação},
 	note = {Faculdade de Informática}
}