Real-Time Emotional Recognition for Sociable Robotics Based on Deep Neural Networks Ensemble

Benamara, Nadir Kamel; Val-Calvo, Mikel; Sánchez, José Luis Guzmán; Díaz‐Morcillo, Alejandro; Vicente, José Manuel Ferrández; Fernández-Jover, Eduardo; Stambouli, Tarik Boudghene

doi:10.1007/978-3-030-19591-5_18

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…HRI was at the core of [83] that introduces a realtime emotion recognition system using a YOLObased facial detection system and an ensemble convolutional neural network. Another work benefited the field of HRI by facing a broadened understanding of brain emotional encoding in order to improve the capabilities of robots to fully engage with the user's emotional reactions [84].…”

Section: Human-machine Interaction and Health-care Applicationsmentioning

confidence: 99%

“…The papers have detected affect from facial cues by robots working in real-world scenarios [83] and speech in order to elicit stress through a set of online interviews, as well as to establish a standard speech corpus to assess emotions across multiple languages [85]. Hence, a number of papers introduced solutions based on the acquisition and processing of physiological signals.…”

Section: Human-machine Interaction and Health-care Applicationsmentioning

confidence: 99%

“…Going up into the interaction realm, one of the next barriers in robotics is to provide sociable robots with the ability to fully engage in emotional interactions with users. In this line, in [83] authors propose a real-time emotion recognition system using a YOLO-based facial detection system and an ensemble CNN. The field of human-robot interactions (HRI) will benefit from a broadened understanding of brain emotional encoding and thus, improve the capabilities of robots to fully engage with the user's emotional reactions.…”

Section: Emotional Interactionmentioning

confidence: 99%

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Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

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Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in

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Section: Human-machine Interaction and Health-care Applicationsmentioning

confidence: 99%

Section: Human-machine Interaction and Health-care Applicationsmentioning

confidence: 99%

Section: Emotional Interactionmentioning

confidence: 99%

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Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

et al. 2020

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“…Additionally, Benamara et al [ 31 ] performed an algorithm for a sociable robot to recognize emotions when interacting with a person in real time. The algorithm detects the face by employing a YOLO framework; then, it converts the resulting image in grayscale, normalized in the range [0, 1].…”

Section: Algorithms Used For Face Recognition and Trackingmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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“…The detected face is then preprocessed: the image is cropped to extract the region of interest, converted from RGB to grayscale, resized to a resolution of 48 × 48 pixels, and finally normalized into a [0,1] range. In the second stage, the preprocessed image is fed into a low level feature extraction layer and a deep convolutional ensemble of neural networks to obtain the emotion classification [42], [43]. This ensemble model was trained on the FER-2013 database [44] achieving a 72.47% accuracy on the test set.…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

Affective Robot Story-Telling Human-Robot Interaction: Exploratory Real-Time Emotion Estimation Analysis Using Facial Expressions and Physiological Signals

et al. 2020

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Affective human-robot interaction is still an active area of research in part due to the great advances in artificial intelligence. Now, the design of autonomous devices that work in real therapeutic environments has become a plausible reality. Affective human-robot interaction requires a robot to analyze the emotional state of the human interlocutor and interpret emotional responses that can be used, not merely in the interaction but, for example, to provoke desired therapeutic responses. It is, therefore, necessary to broaden experimental techniques into more realistic paradigms, where the capacity of emotion estimation can be completely explored. This exploratory paper proposes a realistic experimental paradigm in which the robot employs a dramatic story to evoke emotions in the users, and tests previously self-designed methodologies to be able to make estimates of the users' emotional state in real-time. Regardless of the multiple impediments and restrictions, and all the aspects that could still be improved, this paper can outline the feasibility of the proposed methodology in realistic scenarios.

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Real-Time Emotional Recognition for Sociable Robotics Based on Deep Neural Networks Ensemble

Cited by 12 publications

References 14 publications

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Affective Robot Story-Telling Human-Robot Interaction: Exploratory Real-Time Emotion Estimation Analysis Using Facial Expressions and Physiological Signals

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