Orlandini A., Kristoffersson A., Almquist L., Björkman P., Cesta A., Cortellessa G., Galindo C., Gonzalez-Jimenez J., Gustafsson K., Kiselev A., et al. Exploring use cases for telepresence robots Proceedings of the 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI) Lusanne, Switzerland. Tsui K.M., Desai M., Yanco H.A., Uhlik C. Kristoffersson A., Coradeschi S., Loutfi A. A web-based solution for robotic telepresence. 4.3 Video Face Recognition Solution for Indian Hotel Group Lemon Tree Hotels. Melendez-Fernandez F., Galindo C., Gonzalez-Jimenez J. This paper introduces the NEC video face recognition technology for use in. Social Telepresence Robots: A Narrative Review of Experiments Involving Older Adults before and during the COVID-19 Pandemic. Isabet B., Pino M., Lewis M., Benveniste S., Rigaud A.S. With respect to the other approaches, our model achieves comparable results with a significant reduction in terms of the number of parameters.Īssistive robotics computer vision deep convolutional neural networks emotion recognition face recognition. We have performed training and evaluation (via one- and five-fold cross validation) of the model with five datasets available in the community and one mixed dataset created by taking samples from all of them. Finally, to reduce the dying ReLU problem and improve the stability of the model, we apply an Exponential Linear Unit (ELU) activation function in the initial layers of the model. In addition, Dilated Convolutions expand receptive fields exponentially with preserving resolution, less computation and memory cost to recognize the distinction among facial expressions by capturing the displacement of the pixels. The first remarkable result is the few numbers (i.e., 1.6 Million) of parameters characterizing our model. Specifically, the proposed DL model is based on Residual Convolutional Neural Networks with the combination of Dilated and Standard Convolution Layers. This paper proposes a compact and robust service for Assistive Robotics, called Lightweight EMotion recognitiON (LEMON), which uses image processing, Computer Vision and Deep Learning (DL) algorithms to recognize facial expressions. This aspect makes the design of the systems challenging, requiring a trade-off between the accuracy and the complexity of the adopted models. Most of the low-cost commercial robots, however, although more accessible by users and healthcare facilities, have to balance costs and effectiveness, resulting in under-performing hardware in terms of memory and processing unit. Such systems need to create an empathic interaction with the users therefore, it os required to include an Emotion Recognition (ER) framework which has to run, in near real-time, together with several other intelligent services. The development of a Social Intelligence System based on artificial intelligence is one of the cutting edge technologies in Assistive Robotics.

Lemon face detection