Book ChapterDOI
Happiness and sadness recognition system: preliminary results with an Intel RealSense 3D Sensor
Vinicius Silva,Filomena Soares,João Sena Esteves,Joana Figueiredo,Cristina P. Santos,Ana Paula da Silva Pereira +5 more
- pp 385-395
TLDR
The results point out the adequacy of Intel RealSense for facial features extraction in emotion recognition systems as well as the importance of determining head motion when recognizing sadness.Abstract:
Systems and devices that can recognize human affects have been in development for a considerable time. Facial features are usually extracted by using video data or a Microsoft Kinect sensor. The present paper proposes an emotion recognition system that uses the recent Intel RealSense 3D sensor, whose reliability and validity in the field of emotion recognition has not yet been studied. This preliminary work focus on happiness and sadness. The system extracts the user’s facial Action Units and head motion data. Then, it uses a Support Vector Machine to automatically classify the emotion expressed by the user. The results point out the adequacy of Intel RealSense for facial features extraction in emotion recognition systems as well as the importance of determining head motion when recognizing sadness.read more
Citations
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Journal ArticleDOI
A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities
TL;DR: A facial-expression recognition system to improve the service of the healthcare in a smart city by applying a bandlet transform to a face image to extract sub-bands and producing a feature vector of the face image.
Journal ArticleDOI
Utilising the Intel RealSense Camera for Measuring Health Outcomes in Clinical Research
TL;DR: The Intel RealSense technology is supported to develop robust, objective movement and mobility-based endpoints to enable accurate tracking of the effects of treatment interventions in clinical trials and contributes to pervasive in-clinic and field based health assessment solutions.
Proceedings ArticleDOI
Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense
TL;DR: A hand gesture recognition system based on the data captured by Intel RealSense Front-Facing Camera SR300 that maps depth images to color images based on generalized Hough transform in order to segment hand from a complex background in color images using the depth information.
Journal ArticleDOI
Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets.
Carlos Veiga Almagro,Mario Di Castro,Mario Di Castro,Giacomo Lunghi,Giacomo Lunghi,Raúl Marín Prades,Raúl Marín Prades,Pedro José Sanz Valero,Manuel Perez,Alessandro Masi +9 more
TL;DR: A novel vision system to track and estimate the depth of metallic targets for robotic interventions, designed for on-hand monocular cameras, that increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases.
Proceedings ArticleDOI
Mirroring and recognizing emotions through facial expressions for a RoboKind platform
TL;DR: A system that uses the recent Intel RealSense 3D sensor to promote imitation and recognition of facial expressions, using a RoboKind Zeno R50 robot (ZECA) as a mediator in social activities.
References
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Proceedings ArticleDOI
Real time facial expression recognition in video using support vector machines
Philipp Michel,Rana el Kaliouby +1 more
TL;DR: A real time approach to emotion recognition through facial expression in live video is presented, employing an automatic facial feature tracker to perform face localization and feature extraction and evaluating the method in terms of recognition accuracy.
Proceedings ArticleDOI
Beyond the basic emotions: what should affective computing compute?
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Proceedings ArticleDOI
Facial expression recognition using Support Vector Machines
Muzammil Abdulrahman,Alaa Eleyan +1 more
TL;DR: A facial expression recognition approach based on Principal Component Analysis (PCA) and Local Binary Pattern (LBP) algorithms is proposed that has an average recognition rate of 87% and 77%, respectively.