Ciencias Exactas y Ciencias de la Salud

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Towards a real-time lightweight facial reconstruction model
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-04-30) Hernández Manrique, Victor; González Mendoza, Miguel; emimmayorquin; Vilchis Zapata, Carlos Leonel; Luévano García, Luis Santiago; Rudomín Goldberg, Issac Juan; Escuela de Ingeniería y Ciencias; Campus Monterrey
3D facial reconstruction algorithms are highly effective for diverse uses, including facial recognition, virtual reality, and medical imaging. Yet, the intricacy and computational demands of these methods, coupled with the limited availability of datasets, have confined their use to a specific set of researchers and experts. Furthermore, in response to the demand for resource-efficient solutions, the development of lightweight processes has become a key area of research in computer vision. These models aim to find an equilibrium between model size, computational demands, and accuracy. They offer advantages like efficient use of resources, quicker inference times, and enhanced accessibility. Particularly for 3D facial reconstruction models, lightweight architectures open up possibilities for deployment on less powerful hardware, given that these techniques typically depend on high-performance processors like NVIDIA graphics cards. This thesis presents an overview of 3D face creation, followed by state-of-the-art methods which were analyzed in a comparative table, offering an survey of the fundamental characteristics of each method. As well as that, a benchmark comparison among various leading lightweight models in a facial reconstruction framework, aiming to decrease its computational complexity to enable testing on a mobile device. A quantitative evaluation, such as its losses over the training and testing stages, the inference speed achieved and an evaluation in cutting-edge datasets were presented. Additionally, an analysis on the qualitative aspect, for example, the 3D pose or depth estimation. Those aspects were the base to select a lightweight backbone. Finally, an user interface was developed using Python and Kivy. The model was runned on a constrained-device, such as a single-core of a commercial laptop, to examine its performance. EfficientNetLite was determined as a suitable replacement for the current backbone, since its characteristics and scores obtained over several examinations presented a similar behavior to MobileNet-V1, the default backbone of the facial reconstruction model selected.
Real time distraction detection by facial attributes recognition
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-09) López Esquivel, Andrés Alberto; González Mendoza, Miguel; puemcuervo; Gutiérrez Rodríguez, Andrés Eduardo; Marín Hernández, Antonio; School of Engineering and Sciences; Campus Monterrey; Chang Fernández, Leonardo
The deficit of attention on any critical activity has been a principal source of accidents leading to injuries and fatalities. Therefore the fast detection of it has to be a priority in order to achieve the safe completion of any task and also to ensure the display of the maximum capabilities of the user when achieving the respective activity. While multiple methods has been developed, a new trend of non-intrusive vision based methodologies has been strongly picked by both the research and industrial communities as one with the most potential effectiveness and usability on real life scenarios. In this thesis research, a new attention deficit detection system is presented. Low-weight Machine Learning algorithms will allow the use in remote applications and a variety of goal devices to avoid accidents caused by the lack of attention in complex activities. This research describes its impact, its functioning and previous work. In addition, the system is broken down into its most basic components and its results in various evaluation stages. Finally, its results in semi-real environments are presented and possible applications in real life are discussed, while being compared to state of the art implementations such as CNN’s, Deep learning and other ML implementations

Ciencias Exactas y Ciencias de la Salud

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