Sistema inteligente para la medición del comportamiento humano en relación al cumplimiento del protocolo de bioseguridad Covid-19 implementando tecnología de IoB / Smart system for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing IoB technology

Este proyecto de investigación 2021 desarrollado por la Escuela de Ingeniería en Computación de ITCA-FEPADE, tuvo como objetivo usar las tecnologías para ayudar a mejorar el comportamiento de la comunidad educativa en pandemia Covid-19. Es un sistema inteligente para la medición del comportamiento humano con relación al cumplimiento del protocolo de bioseguridad Covid-19, implementando tecnologías de Internet del Comportamiento IoB, Internet de las Cosas IoT, Business Intelligence, Big Data y reconocimiento facial. La primera fase consistió en la toma de requerimientos y el estudio de investigaciones previas. Posteriormente se diseñó la interfaz del aplicativo que interpreta los datos colectados y la estructura de un dispensador inteligente de alcohol gel para ser impreso en 3D. Finalmente se realizó la programación del sistema y del circuito que conforman el dispositivo. Como resultado se construyó un dispositivo inteligente que mide y alerta la temperatura, dispensa alcohol gel y toma de fotografía para reconocimiento facial en la portación correcta de mascarilla. Incorpora un sistema informático que procesa los datos colectados que son utilizados por la aplicación de Inteligencia de Negocios para analizar el comportamiento de las personas ante el cumplimiento del protocolo de bioseguridad para Covid-19. El resultado del proyecto es un dispositivo inteligente y automatizado, que dotará a la institución de una herramienta innovadora de bajo costo para medir el comportamiento de la población que hace uso de las instalaciones de ITCA-FEPADE Sede Central y contribuirá a prevenir contagios por Covid-19, dando mayor seguridad a un retorno presencial al campus.

This research project was carried out in 2021 by the Escuela de Ingeniería en Computación of ITCA-FEPADE and aimed to use technologies to improve the behavior of the educational community in the context of Covid-19 pandemic. A smart system was development for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing Internet of Behavior (IoB), Internet of Things (IoT), Business Intelligence, Big Data and facial recognition technologies. The first phase consisted on the identification of requirements and previous investigations. Subsequently, the application interface that interprets the collected data and the structure of a smart hand sanitizer dispenser to be printed in 3D was designed. Finally, the programming of the system and the circuit that make up the device was carried out. As a result, a smart device that measures and alerts the body temperature, dispenses hand sanitizer and applies facial recognition for the detection of proper face mask wearing was built. The device also incorporates a computer system that processes the collected data that to analyze the behavior of people in compliance with the biosafety protocol for Covid-19 through the Business Intelligence application. The result of the project was a smart and automated device that will provide the institution an innovative, low-cost tool to measure the behavior of the population that makes use of the ITCA-FEPADE Sede Central facilities and will contribute to preventing Covid-19 infections by giving greater safety to a face-to-face return to the facilities.

Sistema inteligente para la medición del comportamiento humano en relación al cumplimiento del protocolo de bioseguridad Covid-19 implementando tecnología de IoB: aplicación en ITCA-FEPADE Sede Central / Smart system for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing IoB technology: application at ITCA-FEPADE

Este proyecto fue desarrollado por la Escuela de Ingeniería en Computación de ITCA-FEPADE y tuvo como objetivo usar las tecnologías para ayudar a mejorar el comportamiento de la comunidad educativa en pandemia Covid-19. Es un sistema inteligente para la medición del comportamiento humano con relación al cumplimiento del protocolo de bioseguridad Covid-19, implementando tecnologías de Internet del Comportamiento IoB, Internet de las Cosas IoT, Business Intelligence, Big Data y reconocimiento facial. La primera fase consistió en la toma de requerimientos e investigaciones previas. Posteriormente se diseñó la interfaz del aplicativo que interpreta los datos colectados y la estructura de un dispensador inteligente de alcohol gel para ser impreso en 3D. Finalmente se realizó la programación del sistema y del circuito que conforman el dispositivo. Como resultado se construyó un dispositivo inteligente que mide y alerta la temperatura, dispensa alcohol gel y toma de fotografía para reconocimiento facial en la portación correcta de mascarilla.

This research project was carried out in 2021 by the Escuela de Ingeniería en Computación of ITCA-FEPADE and aimed to use technologies to improve the behavior of the educational community in the context of Covid-19 pandemic. A smart system was development for measuring human behavior in relation to compliance with the Covid-19 biosafety protocol, implementing Internet of Behavior (IoB), Internet of Things (IoT), Business Intelligence, Big Data and facial recognition technologies. The first phase consisted on the identification of requirements and previous investigations. Subsequently, the application interface that interprets the collected data and the structure of a smart hand sanitizer dispenser to be printed in 3D was designed. Finally, the programming of the system and the circuit that make up the device was carried out. As a result, a smart device that measures and alerts the body temperature, dispenses hand sanitizer and applies facial recognition for the detection of proper face mask wearing was built.

Tecnología de reconocimiento facial y sus riesgos en los derechos humanos / Facial recognition technology and its risks to human rights / A tecnologia de reconhecimento facial e seus riscos para os direitos humanos

El desarrollo de tecnologías dinamizadas por la inteligencia artificial (IA) representa un desafío adaptativo para ciencias tradicionales y rígidas como el derecho. Debido a las características de los diversos métodos o procedimientos usados de forma automatizada, se presenta una relación antagónica entre implementación de herramientas de reconocimiento facial y los derechos considerados garantías constitucionales y fundamentales en el sistema de derechos humanos. El objetivo es describir el funcionamiento de los sistemas de visión involucrados en la IA, presente principalmente en las herramientas de reconocimiento facial, examinando la manera como se relacionan con el derecho penal y reconociendo los riesgos a los derechos humanos en este proceso. Para ello, se usó una metodología cualitativa-inductiva, realizando análisis de fuentes primarias y secundarias, estudios de caso y legislaciones de diversas jurisdicciones relacionadas con reconocimiento facial y su aplicación en las etapas de indagación e investigación en el proceso penal. Como resultado se obtuvo que en dichas etapas existe un riesgo a las garantías de un debido proceso y de no discriminación.

The development of technologies powered by artificial intelligence (AI) represents an adaptive challenge for traditional and rigid sciences such as law. Due to the characteristics of the various methods or procedures used in an automated way, there is an antagonistic relationship between the implementation of facial recognition tools and the rights considered constitutional and fundamental guarantees in the human rights system. The objective is to describe the functioning of the vision systems involved in AI, mainly present in facial recognition tools, examining how they relate to criminal law and recognizing the risks to human rights in this process. For this purpose, a qualitative-inductive methodology was used, analyzing primary and secondary sources, case studies and legislation from various jurisdictions related to facial recognition and its application in the investigation and inquiry stages of the criminal process. As a result, it was obtained that in such stages there is a risk to the guarantees of due process and non-discrimination.

O desenvolvimento de tecnologias impulsionadas pela inteligência artificial (IA) representa um desafio adaptativo para as ciências tradicionais e rígidas, como o direito. Devido às características dos vários métodos ou procedimentos utilizados de forma automatizada, existe uma relação antagônica entre a implementação de ferramentas de reconhecimento facial e os direitos considerados garantias constitucionais e fundamentais no sistema de direitos humanos. O objetivo é descrever o funcionamento dos sistemas de visão envolvidos na IA, principalmente presentes nas ferramentas de reconhecimento facial, examinando como eles se relacionam com o direito penal e reconhecendo os riscos aos direitos humanos neste processo. Para este fim, foi utilizada uma metodologia qualitativa-indutora, analisando fontes primárias e secundárias, estudos de casos e legislação de várias jurisdições relacionadas ao reconhecimento facial e sua aplicação nas fases de investigação e inquérito de processos criminais. Como resultado, foi obtido que nestas etapas há um risco para as garantias de um processo justo e não-discriminação.

Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling.

The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.

Applicability of forensic facial approximation in the recognition process of unclaimed victims.

BACKGROUND: Identifying bodies in a state of putrefaction, skeletonization or mutilation is often difficult. In these cases, it is possible to use auxiliary methods such as forensic facial approximation, considering the possibility of recognition by a relative or acquaintance, helping to obtain ante-mortem data for the identification process. The aims of the present study were to evaluate the capacity of recognition of individuals from digital facial approximation and to verify the association between the level of understanding of the issue by evaluators and the recognition success index. METHODS: 16 skulls with previous photographic records were selected and then utilized for three-dimensional approximation using the digital technique, scanned by photogrammetry, and reconstructed by computerized method using open-source software. Twenty evaluators tried to recognize the facial approximation performed from images present in the photospreads. RESULTS: The mean overall score was 23.75%, and it was observed that in only five approximations (31.24%) the option of correct recognition of the victim was the one that obtained the highest number of selections. False positives and negatives corresponded, respectively, to 11.56% and 12.5%. CONCLUSIONS: It can be concluded that the methodology can provide recognition albeit in low numbers, and permitting the acquisition of ante-mortem data for the proper process of human identification through primary methods.

3D forensic facial approximation: Implementation protocol in a forensic activity.

The primary objective of this paper is to report on the successful implementation of forensic facial approximation in a real case in the forensic context. A three-dimensional (3D) facial approximation protocol of the skull was performed with free software, applying techniques in a virtual environment that have already been consolidated in the literature. The skull was scanned with the photogrammetry technique, the digital replica was imported in the Blender software (Blender Foundation, Amsterdam) and individualized model sketches of the face were traced with the MakeHuman software (MakeHuman Org) according to the anthropological profile of the victim. The face created was imported in Blender, where it was adapted, modeled, and sculpted on the 3D skull and its soft tissue markers, using an American open-source application of the technique in the digital environment. The face created in a virtual environment was recognized and legal identification procedures were started, resulting in the more agile delivery of the disappeared body to its next of kin. It is therefore concluded that facial approximation may not be a primary method of human identification, but it can be satisfactorily applied in the forensic field as an individual recognition resource. It has great value in narrowing the search, reducing the number of alleged victims, and leading to identification tests, therefore significantly reducing the number of genetic DNA (deoxyribonucleic acid) tests-which are considered costly for the State or Federation-and consequently reducing the waiting time before delivery of the body to its family.

Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition.

An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fear, anger, and disgust. Inspired by the Facial Action Coding System (FACS) and the Moving Picture Experts Group 4th standard (MPEG-4), an initial set of 89 features was proposed. These features are normalized distances and angles in 2D and 3D computed from 22 facial landmarks. To select a minimum set of features with the maximum classification accuracy, two selection methods and four classifiers were tested. The first selection method, principal component analysis (PCA), obtained 39 features. The second selection method, a genetic algorithm (GA), obtained 47 features. The experiments ran on the Bosphorus and UIVBFED data sets with 86.62% and 93.92% median accuracy, respectively. Our main finding is that the reduced feature set obtained by the GA is the smallest in comparison with other methods of comparable accuracy. This has implications in reducing the time of recognition.