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 use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2023
Marín-Morales, J.; Llanes-Jurado, J.; Minissi, M. E.; Gómez-Zaragozá, L.; Altozano, A.; Alcaniz, M.
Gaze and Head Movement Patterns of Depressive Symptoms During Conversations with Emotional Virtual Humans Proceedings Article
In: Int. Conf. Affect. Comput. Intell. Interact., ACII, Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835032743-4 (ISBN).
Abstract | Links | BibTeX | Tags: Biomarkers, Clustering, Clusterings, Computational Linguistics, Depressive disorder, Depressive symptom, E-Learning, Emotion elicitation, Eye movements, Gaze movements, K-means clustering, Language Model, Large language model, large language models, Learning systems, Mental health, Multivariant analysis, Signal processing, Statistical learning, virtual human, Virtual humans, Virtual Reality
@inproceedings{marin-morales_gaze_2023,
title = {Gaze and Head Movement Patterns of Depressive Symptoms During Conversations with Emotional Virtual Humans},
author = {J. Marín-Morales and J. Llanes-Jurado and M. E. Minissi and L. Gómez-Zaragozá and A. Altozano and M. Alcaniz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184656388&doi=10.1109%2fACII59096.2023.10388134&partnerID=40&md5=143cdd8530e17a7b64bdf88f3a0496ab},
doi = {10.1109/ACII59096.2023.10388134},
isbn = {979-835032743-4 (ISBN)},
year = {2023},
date = {2023-01-01},
booktitle = {Int. Conf. Affect. Comput. Intell. Interact., ACII},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Depressive symptoms involve dysfunctional social attitudes and heightened negative emotional states. Identifying biomarkers requires data collection in realistic environments that activate depression-specific phenomena. However, no previous research analysed biomarkers in combination with AI-powered conversational virtual humans (VH) for mental health assessment. This study aims to explore gaze and head movements patterns related to depressive symptoms during conversations with emotional VH. A total of 105 participants were evenly divided into a control group and a group of subjects with depressive symptoms (SDS). They completed six semi-guided conversations designed to evoke basic emotions. The VHs were developed using a cognitive-inspired framework, enabling real-time voice-based conversational interactions powered by a Large Language Model, and including emotional facial expressions and lip synchronization. They have embedded life-history, context, attitudes, emotions and motivations. Signal processing techniques were applied to obtain gaze and head movements features, and heatmaps were generated. Then, parametric and non-parametric statistical tests were applied to evaluate differences between groups. Additionally, a two-dimensional t-SNE embedding was created and combined with k-means clustering. Results indicate that SDS exhibited shorter blinks and longer saccades. The control group showed affiliative lateral head gyros and accelerations, while the SDS demonstrated stress-related back-and-forth movements. SDS also displayed the avoidance of eye contact. The exploratory multivariate statistical unsupervised learning achieved 72.3% accuracy. The present study analyse biomarkers in affective processes with multiple social contextual factors and information modalities in ecological environments, and enhances our understanding of gaze and head movements patterns in individuals with depressive symptoms, ultimately contributing to the development of more effective assessments and intervention strategies. © 2023 IEEE.},
keywords = {Biomarkers, Clustering, Clusterings, Computational Linguistics, Depressive disorder, Depressive symptom, E-Learning, Emotion elicitation, Eye movements, Gaze movements, K-means clustering, Language Model, Large language model, large language models, Learning systems, Mental health, Multivariant analysis, Signal processing, Statistical learning, virtual human, Virtual humans, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Depressive symptoms involve dysfunctional social attitudes and heightened negative emotional states. Identifying biomarkers requires data collection in realistic environments that activate depression-specific phenomena. However, no previous research analysed biomarkers in combination with AI-powered conversational virtual humans (VH) for mental health assessment. This study aims to explore gaze and head movements patterns related to depressive symptoms during conversations with emotional VH. A total of 105 participants were evenly divided into a control group and a group of subjects with depressive symptoms (SDS). They completed six semi-guided conversations designed to evoke basic emotions. The VHs were developed using a cognitive-inspired framework, enabling real-time voice-based conversational interactions powered by a Large Language Model, and including emotional facial expressions and lip synchronization. They have embedded life-history, context, attitudes, emotions and motivations. Signal processing techniques were applied to obtain gaze and head movements features, and heatmaps were generated. Then, parametric and non-parametric statistical tests were applied to evaluate differences between groups. Additionally, a two-dimensional t-SNE embedding was created and combined with k-means clustering. Results indicate that SDS exhibited shorter blinks and longer saccades. The control group showed affiliative lateral head gyros and accelerations, while the SDS demonstrated stress-related back-and-forth movements. SDS also displayed the avoidance of eye contact. The exploratory multivariate statistical unsupervised learning achieved 72.3% accuracy. The present study analyse biomarkers in affective processes with multiple social contextual factors and information modalities in ecological environments, and enhances our understanding of gaze and head movements patterns in individuals with depressive symptoms, ultimately contributing to the development of more effective assessments and intervention strategies. © 2023 IEEE.
2017
Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi
Human Skin Detection through Correlation Rules between the YCb and YCr Subspaces Based on Dynamic Color Clustering Journal Article
In: Computer Vision and Image Understanding, vol. 155, pp. 33–42, 2017, ISSN: 1077-3142.
Abstract | Links | BibTeX | Tags: Classification, Clustering, Correlation rules, Skin detection, YCbCr color space
@article{brancatiHumanSkinDetection2017,
title = {Human Skin Detection through Correlation Rules between the YCb and YCr Subspaces Based on Dynamic Color Clustering},
author = { Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},
doi = {10.1016/j.cviu.2016.12.001},
issn = {1077-3142},
year = {2017},
date = {2017-02-01},
urldate = {2017-02-05},
journal = {Computer Vision and Image Understanding},
volume = {155},
pages = {33--42},
abstract = {This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.},
keywords = {Classification, Clustering, Correlation rules, Skin detection, YCbCr color space},
pubstate = {published},
tppubtype = {article}
}
This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.
Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi
Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering Journal Article
In: Computer Vision and Image Understanding, vol. 155, pp. 33–42, 2017, ISSN: 1077-3142.
Abstract | Links | BibTeX | Tags: Classification, Clustering, Correlation rules, Skin detection, YCbCr color space
@article{brancati_human_2017,
title = {Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering},
author = {Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},
url = {https://www.sciencedirect.com/science/article/pii/S1077314216301989},
doi = {10.1016/j.cviu.2016.12.001},
issn = {1077-3142},
year = {2017},
date = {2017-02-01},
urldate = {2017-02-05},
journal = {Computer Vision and Image Understanding},
volume = {155},
pages = {33–42},
abstract = {This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.},
keywords = {Classification, Clustering, Correlation rules, Skin detection, YCbCr color space},
pubstate = {published},
tppubtype = {article}
}
This paper presents a novel rule-based skin detection method that works in the YCbCr color space. The method is based on correlation rules that evaluate the combinations of chrominance values to identify the skin pixels in the YCb and YCr subspaces. The correlation rules depend on the shape and size of dynamically generated skin color clusters, which are computed on a statistical basis in the YCb and YCr subspaces for each single image, and represent the areas that include most of the candidate skin pixels. Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters. Moreover, the qualitative analysis shows that the method achieves satisfactory results also in critical scenarios, including severe variations in illumination conditions.
Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi
Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions Proceedings Article
In: Krasnoproshin, Viktor V.; Ablameyko, Sergey V. (Ed.): Pattern Recognition and Information Processing, pp. 27–35, Springer International Publishing, Cham, 2017, ISBN: 978-3-319-54220-1.
Abstract | Links | BibTeX | Tags: Clustering, Skin detection, YCbCr color space
@inproceedings{brancatiDynamicColourClustering2017,
title = {Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions},
author = { Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},
editor = { Viktor V. Krasnoproshin and Sergey V. Ablameyko},
doi = {10.1007/978-3-319-54220-1_3},
isbn = {978-3-319-54220-1},
year = {2017},
date = {2017-01-01},
booktitle = {Pattern Recognition and Information Processing},
pages = {27--35},
publisher = {Springer International Publishing},
address = {Cham},
series = {Communications in Computer and Information Science},
abstract = {Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color space, by taking into account the lighting conditions. The method is based on the identification of skin color clusters in the YCb and YCr subspaces. The experimental results, carried out on two publicly available databases, show that the proposed method is robust against illumination changes and achieves satisfactory results in terms of both qualitative and quantitative performance evaluation parameters.},
keywords = {Clustering, Skin detection, YCbCr color space},
pubstate = {published},
tppubtype = {inproceedings}
}
Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color space, by taking into account the lighting conditions. The method is based on the identification of skin color clusters in the YCb and YCr subspaces. The experimental results, carried out on two publicly available databases, show that the proposed method is robust against illumination changes and achieves satisfactory results in terms of both qualitative and quantitative performance evaluation parameters.
Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi
Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions Proceedings Article
In: Krasnoproshin, Viktor V.; Ablameyko, Sergey V. (Ed.): Pattern Recognition and Information Processing, pp. 27–35, Springer International Publishing, Cham, 2017, ISBN: 978-3-319-54220-1.
Abstract | Links | BibTeX | Tags: Clustering, Skin detection, YCbCr color space
@inproceedings{brancati_dynamic_2017,
title = {Dynamic Colour Clustering for Skin Detection Under Different Lighting Conditions},
author = {Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},
editor = {Viktor V. Krasnoproshin and Sergey V. Ablameyko},
doi = {10.1007/978-3-319-54220-1_3},
isbn = {978-3-319-54220-1},
year = {2017},
date = {2017-01-01},
booktitle = {Pattern Recognition and Information Processing},
pages = {27–35},
publisher = {Springer International Publishing},
address = {Cham},
series = {Communications in Computer and Information Science},
abstract = {Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color space, by taking into account the lighting conditions. The method is based on the identification of skin color clusters in the YCb and YCr subspaces. The experimental results, carried out on two publicly available databases, show that the proposed method is robust against illumination changes and achieves satisfactory results in terms of both qualitative and quantitative performance evaluation parameters.},
keywords = {Clustering, Skin detection, YCbCr color space},
pubstate = {published},
tppubtype = {inproceedings}
}
Skin detection is an important process in many applications like hand gesture recognition, face detection and ego-vision systems. This paper presents a new skin detection method based on a dynamic generation of the skin cluster range in the YCbCr color space, by taking into account the lighting conditions. The method is based on the identification of skin color clusters in the YCb and YCr subspaces. The experimental results, carried out on two publicly available databases, show that the proposed method is robust against illumination changes and achieves satisfactory results in terms of both qualitative and quantitative performance evaluation parameters.
2016
Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi
Dynamic Clustering for Skin Detection in YCbCr Colour Space Proceedings Article
In: 2016 Proceeding of the 13th International Conference on Pattern Recognition and Information Processing (PRIP), pp. 49–53, Minsk: Publishing Center of BSU, Minsk, Belarus, 2016, ISBN: 978-985-553-383-3.
Abstract | BibTeX | Tags: Clustering, Skin detection, YCbCr color space
@inproceedings{brancatiDynamicClusteringSkin2016,
title = {Dynamic Clustering for Skin Detection in YCbCr Colour Space},
author = { Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},
isbn = {978-985-553-383-3},
year = {2016},
date = {2016-10-01},
booktitle = {2016 Proceeding of the 13th International Conference on Pattern Recognition and Information Processing (PRIP)},
pages = {49--53},
publisher = {Minsk: Publishing Center of BSU},
address = {Minsk, Belarus},
abstract = {This paper presents a new approach for skin detection in colour images. The method is based on the building of a dynamic clustering in the YCbCr colour space, taking into account the illumination conditions of the examined image. The results of a comparative evaluation on a publicly available database, show that the proposed method outperforms well known rule based static methods, both in qualitative and quantitative terms.},
keywords = {Clustering, Skin detection, YCbCr color space},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new approach for skin detection in colour images. The method is based on the building of a dynamic clustering in the YCbCr colour space, taking into account the illumination conditions of the examined image. The results of a comparative evaluation on a publicly available database, show that the proposed method outperforms well known rule based static methods, both in qualitative and quantitative terms.
Brancati, Nadia; Pietro, Giuseppe De; Frucci, Maria; Gallo, Luigi
Dynamic clustering for skin detection in YCbCr colour space Proceedings Article
In: 2016 Proceeding of the 13th international conference on Pattern Recognition and Information Processing (PRIP), pp. 49–53, Minsk: Publishing Center of BSU, Minsk, Belarus, 2016, ISBN: 978-985-553-383-3.
Abstract | Links | BibTeX | Tags: Clustering, Skin detection, YCbCr color space
@inproceedings{brancati_dynamic_2016,
title = {Dynamic clustering for skin detection in YCbCr colour space},
author = {Nadia Brancati and Giuseppe De Pietro and Maria Frucci and Luigi Gallo},
url = {http://elib.bsu.by/bitstream/123456789/158528/1/Brancati_Pietro_Frucci_Gallo.pdf},
isbn = {978-985-553-383-3},
year = {2016},
date = {2016-10-01},
booktitle = {2016 Proceeding of the 13th international conference on Pattern Recognition and Information Processing (PRIP)},
pages = {49–53},
publisher = {Minsk: Publishing Center of BSU},
address = {Minsk, Belarus},
abstract = {This paper presents a new approach for skin detection in colour images. The method is based on the building of a dynamic clustering in the YCbCr colour space, taking into account the illumination conditions of the examined image. The results of a comparative evaluation on a publicly available database, show that the proposed method outperforms well known rule based static methods, both in qualitative and quantitative terms.},
keywords = {Clustering, Skin detection, YCbCr color space},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new approach for skin detection in colour images. The method is based on the building of a dynamic clustering in the YCbCr colour space, taking into account the illumination conditions of the examined image. The results of a comparative evaluation on a publicly available database, show that the proposed method outperforms well known rule based static methods, both in qualitative and quantitative terms.