AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2022
Augello, Agnese; Bella, Giulia Di; Infantino, Ignazio; Pilato, Giovanni; Vitale, Gianluigi
Multimodal Mood Recognition for Assistive Scenarios Proceedings Article
In: F.F., Samsonovich A. V. Ramos Corchado (Ed.): Procedia Computer Science, pp. 510–517, Elsevier B.V., 2022.
Abstract | Links | BibTeX | Tags: Assistive Robots, Emotion Analysis, Emotion Recognition, Mood
@inproceedings{augelloMultimodalMoodRecognition2022,
title = {Multimodal Mood Recognition for Assistive Scenarios},
author = { Agnese Augello and Giulia Di Bella and Ignazio Infantino and Giovanni Pilato and Gianluigi Vitale},
editor = { Samsonovich A.V. Ramos Corchado F.F.},
doi = {10.1016/j.procs.2022.11.098},
year = {2022},
date = {2022-01-01},
booktitle = {Procedia Computer Science},
volume = {213},
pages = {510--517},
publisher = {Elsevier B.V.},
abstract = {We illustrate a system performing multimodal human emotion detection from video input through the integration of audio emotional recognition, text emotional recognition, facial emotional recognition, and emotional recognition from a spectrogram. The outcomes of the four emotion recognition modalities are compared, and a final evaluation provides the most likely perceived emotion. The system has been designed to be easily implemented on cheap mini-computer based boards. It is conceived to be used as auxiliary tool in the field of telemedicine to remotely monitor the mood of patients and observe their healing process, which is closely related to their emotional condition. textcopyright 2022 The Author(s).},
keywords = {Assistive Robots, Emotion Analysis, Emotion Recognition, Mood},
pubstate = {published},
tppubtype = {inproceedings}
}
We illustrate a system performing multimodal human emotion detection from video input through the integration of audio emotional recognition, text emotional recognition, facial emotional recognition, and emotional recognition from a spectrogram. The outcomes of the four emotion recognition modalities are compared, and a final evaluation provides the most likely perceived emotion. The system has been designed to be easily implemented on cheap mini-computer based boards. It is conceived to be used as auxiliary tool in the field of telemedicine to remotely monitor the mood of patients and observe their healing process, which is closely related to their emotional condition. textcopyright 2022 The Author(s).
Augello, Agnese; Bella, Giulia Di; Infantino, Ignazio; Pilato, Giovanni; Vitale, Gianluigi
Multimodal Mood Recognition for Assistive Scenarios Proceedings Article
In: F.F., Samsonovich A. V. Ramos Corchado (Ed.): Procedia Computer Science, pp. 510–517, Elsevier B.V., 2022.
Abstract | Links | BibTeX | Tags: Assistive Robots, Emotion Analysis, Emotion Recognition, Mood
@inproceedings{augello_multimodal_2022,
title = {Multimodal Mood Recognition for Assistive Scenarios},
author = {Agnese Augello and Giulia Di Bella and Ignazio Infantino and Giovanni Pilato and Gianluigi Vitale},
editor = {Samsonovich A. V. Ramos Corchado F.F.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146111070&doi=10.1016%2fj.procs.2022.11.098&partnerID=40&md5=5f02507ae4efe3f3fe476ce3f7dbc63d},
doi = {10.1016/j.procs.2022.11.098},
year = {2022},
date = {2022-01-01},
booktitle = {Procedia Computer Science},
volume = {213},
pages = {510–517},
publisher = {Elsevier B.V.},
abstract = {We illustrate a system performing multimodal human emotion detection from video input through the integration of audio emotional recognition, text emotional recognition, facial emotional recognition, and emotional recognition from a spectrogram. The outcomes of the four emotion recognition modalities are compared, and a final evaluation provides the most likely perceived emotion. The system has been designed to be easily implemented on cheap mini-computer based boards. It is conceived to be used as auxiliary tool in the field of telemedicine to remotely monitor the mood of patients and observe their healing process, which is closely related to their emotional condition. © 2022 The Author(s).},
keywords = {Assistive Robots, Emotion Analysis, Emotion Recognition, Mood},
pubstate = {published},
tppubtype = {inproceedings}
}
We illustrate a system performing multimodal human emotion detection from video input through the integration of audio emotional recognition, text emotional recognition, facial emotional recognition, and emotional recognition from a spectrogram. The outcomes of the four emotion recognition modalities are compared, and a final evaluation provides the most likely perceived emotion. The system has been designed to be easily implemented on cheap mini-computer based boards. It is conceived to be used as auxiliary tool in the field of telemedicine to remotely monitor the mood of patients and observe their healing process, which is closely related to their emotional condition. © 2022 The Author(s).
2017
Gentile, Vito; Milazzo, Fabrizio; Sorce, Salvatore; Gentile, Antonio; Augello, Agnese; Pilato, Giovanni
Body Gestures and Spoken Sentences: A Novel Approach for Revealing User's Emotions Proceedings Article
In: Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017, pp. 69–72, Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-1-5090-4896-0.
Abstract | Links | BibTeX | Tags: Emotion Analysis, Emotion Recognition, Facial Expressions, Gestural user interfaces, Human computer interaction, Semantic Computing, Sentiment Analysis
@inproceedings{gentileBodyGesturesSpoken2017,
title = {Body Gestures and Spoken Sentences: A Novel Approach for Revealing User's Emotions},
author = { Vito Gentile and Fabrizio Milazzo and Salvatore Sorce and Antonio Gentile and Agnese Augello and Giovanni Pilato},
doi = {10.1109/ICSC.2017.14},
isbn = {978-1-5090-4896-0},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017},
pages = {69--72},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have contributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be used to reveal user's emotions from gestures. textcopyright 2017 IEEE.},
keywords = {Emotion Analysis, Emotion Recognition, Facial Expressions, Gestural user interfaces, Human computer interaction, Semantic Computing, Sentiment Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have contributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be used to reveal user's emotions from gestures. textcopyright 2017 IEEE.
Gentile, Vito; Milazzo, Fabrizio; Sorce, Salvatore; Gentile, Antonio; Augello, Agnese; Pilato, Giovanni
Body Gestures and Spoken Sentences: A Novel Approach for Revealing User's Emotions Proceedings Article
In: Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017, pp. 69–72, Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-1-5090-4896-0.
Abstract | Links | BibTeX | Tags: Emotion Analysis, Emotion Recognition, Facial Expressions, Gestural user interfaces, Human computer interaction, Semantic Computing, Sentiment Analysis
@inproceedings{gentile_body_2017,
title = {Body Gestures and Spoken Sentences: A Novel Approach for Revealing User's Emotions},
author = {Vito Gentile and Fabrizio Milazzo and Salvatore Sorce and Antonio Gentile and Agnese Augello and Giovanni Pilato},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018255013&doi=10.1109%2fICSC.2017.14&partnerID=40&md5=23d8bb016146afe5e384b12d84f3fb85},
doi = {10.1109/ICSC.2017.14},
isbn = {978-1-5090-4896-0},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings - IEEE 11th International Conference on Semantic Computing, ICSC 2017},
pages = {69–72},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have contributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be used to reveal user's emotions from gestures. © 2017 IEEE.},
keywords = {Emotion Analysis, Emotion Recognition, Facial Expressions, Gestural user interfaces, Human computer interaction, Semantic Computing, Sentiment Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last decade, there has been a growing interest in emotion analysis research, which has been applied in several areas of computer science. Many authors have contributed to the development of emotion recognition algorithms, considering textual or non verbal data as input, such as facial expressions, gestures or, in the case of multi-modal emotion recognition, a combination of them. In this paper, we describe a method to detect emotions from gestures using the skeletal data obtained from Kinect-like devices as input, as well as a textual description of their meaning. The experimental results show that the correlation existing between body movements and spoken user sentence(s) can be used to reveal user's emotions from gestures. © 2017 IEEE.
2013
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Rizzo, Riccardo; Vella, Filippo
Introducing a Creative Process on a Cognitive Architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 6, pp. 131–139, 2013, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: Cognitive Architectures, Computational Creativity, Creative Process, Emotion Analysis, Motivation
@article{augelloIntroducingCreativeProcess2013,
title = {Introducing a Creative Process on a Cognitive Architecture},
author = { Agnese Augello and Ignazio Infantino and Giovanni Pilato and Riccardo Rizzo and Filippo Vella},
doi = {10.1016/j.bica.2013.05.011},
issn = {2212683X},
year = {2013},
date = {2013-01-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {6},
pages = {131--139},
abstract = {In this paper we present a system that implements a creative behavior on a cognitive architecture. It is aimed at creating digital art images from snapshots of a human subject, simulating a simple creative process. Such a process starts from a Training Phase that creates a set of image filter sequences. This phase is oriented to approximate some painting styles obtained from famous images and portraits of the past. The learned filter sequences are then used during the Production Phase. During this subsequent phase, the "artificial artist" interacts with the subject trying to "catch" the human emotions that drive the creation of the portrait. The artist processes feedbacks from the user according to the cognitive model Psi and its implementation of the motivations. These motivations influence further modifications of the applied filter sequences achieving an evolution of the artificial artist. textcopyright 2013 Elsevier B.V. All rights reserved.},
keywords = {Cognitive Architectures, Computational Creativity, Creative Process, Emotion Analysis, Motivation},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a system that implements a creative behavior on a cognitive architecture. It is aimed at creating digital art images from snapshots of a human subject, simulating a simple creative process. Such a process starts from a Training Phase that creates a set of image filter sequences. This phase is oriented to approximate some painting styles obtained from famous images and portraits of the past. The learned filter sequences are then used during the Production Phase. During this subsequent phase, the "artificial artist" interacts with the subject trying to "catch" the human emotions that drive the creation of the portrait. The artist processes feedbacks from the user according to the cognitive model Psi and its implementation of the motivations. These motivations influence further modifications of the applied filter sequences achieving an evolution of the artificial artist. textcopyright 2013 Elsevier B.V. All rights reserved.
Augello, Agnese; Infantino, Ignazio; Pilato, Giovanni; Rizzo, Riccardo; Vella, Filippo
Introducing a creative process on a cognitive architecture Journal Article
In: Biologically Inspired Cognitive Architectures, vol. 6, pp. 131–139, 2013, ISSN: 2212683X.
Abstract | Links | BibTeX | Tags: Cognitive Architectures, Computational Creativity, Creative Process, Emotion Analysis, Motivation
@article{augello_introducing_2013,
title = {Introducing a creative process on a cognitive architecture},
author = {Agnese Augello and Ignazio Infantino and Giovanni Pilato and Riccardo Rizzo and Filippo Vella},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883260536&doi=10.1016%2fj.bica.2013.05.011&partnerID=40&md5=2df560e32408e4fe27f412efe0a12b19},
doi = {10.1016/j.bica.2013.05.011},
issn = {2212683X},
year = {2013},
date = {2013-01-01},
journal = {Biologically Inspired Cognitive Architectures},
volume = {6},
pages = {131–139},
abstract = {In this paper we present a system that implements a creative behavior on a cognitive architecture. It is aimed at creating digital art images from snapshots of a human subject, simulating a simple creative process. Such a process starts from a Training Phase that creates a set of image filter sequences. This phase is oriented to approximate some painting styles obtained from famous images and portraits of the past. The learned filter sequences are then used during the Production Phase. During this subsequent phase, the "artificial artist" interacts with the subject trying to "catch" the human emotions that drive the creation of the portrait. The artist processes feedbacks from the user according to the cognitive model Psi and its implementation of the motivations. These motivations influence further modifications of the applied filter sequences achieving an evolution of the artificial artist. © 2013 Elsevier B.V. All rights reserved.},
keywords = {Cognitive Architectures, Computational Creativity, Creative Process, Emotion Analysis, Motivation},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a system that implements a creative behavior on a cognitive architecture. It is aimed at creating digital art images from snapshots of a human subject, simulating a simple creative process. Such a process starts from a Training Phase that creates a set of image filter sequences. This phase is oriented to approximate some painting styles obtained from famous images and portraits of the past. The learned filter sequences are then used during the Production Phase. During this subsequent phase, the "artificial artist" interacts with the subject trying to "catch" the human emotions that drive the creation of the portrait. The artist processes feedbacks from the user according to the cognitive model Psi and its implementation of the motivations. These motivations influence further modifications of the applied filter sequences achieving an evolution of the artificial artist. © 2013 Elsevier B.V. All rights reserved.