Development of Yawning Detection Algorithm for Normal Lighting Condition and IR Condition

Drowsiness monitoring system has been widely used in this current technology to monitor the driver’s state while driving. This paper presents a drowsiness detection method through the activity of yawning for both normal lighting condition and Infrared (IR) condition. Development of the algorithm consists of several steps. Initially, the detection of face and mouth implementing the Viola-Jones algorithm takes place. For IR condition, the mouth is detected by applying the geometrical measurements of a face. After the detection process is done, the tracking process for both face and mouth takes place utilizing the Kanade-Lucas-Tomasi (KLT) algorithm which is basically a point tracking algorithm. Based on the tracked mouth, the region of interest (ROI) is selected which is to be used as an input image in the image processing step in order to get a clearer image of the mouth. From the finalized mouth image in the preprocessing step, the properties of the image are further used in the yawning detection step. In the indication of yawning, the height of the mouth opening reading score is observed. The performance of the proposed method is tested on 5 subjects and achieved an overall accuracy of 98.89% for normal lighting condition and 95.29% for IR condition

Driver fatigue detection via differential evolution extreme learning machine technique

Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale samples in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the sample database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM

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