Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

Micro-expression Recognition using Spatiotemporal Texture Map and Motion Magnification

Micro-expressions are short-lived, rapid facial expressions that are exhibited by individuals when they are in high stakes situations. Studying these micro-expressions is important as these cannot be modified by an individual and hence offer us a peek into what the individual is actually feeling and thinking as opposed to what he/she is trying to portray. The spotting and recognition of micro-expressions has applications in the fields of criminal investigation, psychotherapy, education etc. However due to micro-expressions’ short-lived and rapid nature; spotting, recognizing and classifying them is a major challenge. In this paper, we design a hybrid approach for spotting and recognizing micro-expressions by utilizing motion magnification using Eulerian Video Magnification and Spatiotemporal Texture Map (STTM). The validation of this approach was done on the spontaneous micro-expression dataset, CASMEII in comparison with the baseline. This approach achieved an accuracy of 80% viz. an increase by 5% as compared to the existing baseline by utilizing 10-fold cross validation using Support Vector Machines (SVM) with a linear kernel

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