FRANCESCO DI LUZIO

Dottore di ricerca

ciclo: XXXVI


Titolo della tesi: Deep Neural Networks for Emotion Recognition and Behavioral Analysis

In the domain of academic research, this doctoral thesis embarks on a rigorous investigation into the utilization of deep neural networks for the dual purposes of behavioral analysis and emotion recognition. At its core, this study grapples with the convergence of advanced technology and the intricacies of human expression. A pivotal focus of the research is the nuanced exploration of human emotions. It represents a critical endeavor as emotions are a foundational aspect of human cognition. The central objective is the deployment of facial landmarks as the primary source of data for emotion classification. This classification hinges on the integration of Convolutional Neural Networks and Long Short-Term Memory networks, harnessing their capabilities for the analysis of emotion within the context of video data. The approach transcends mere classification, striving for a profound interpretation of deep learning models for human emotions studying the inner mechanism behind the emotional feeling. Each facial gesture and micro-expression is meticulously dissected and decoded by these innovative models, effectively converting visual data into quantifiable emotional states. The aim is to decipher the intricate nuances of human emotional expression, mapping these to specific labels within the emotional spectrum. In detail, this project casts a discerning eye toward the aspect of model explainability. In an era where artificial intelligence is heavily relied upon, it is imperative to comprehend the inner workings of these models. The research seeks to elucidate the black box by providing insights into the decision-making processes of these deep neural networks. By rendering the models' decision pathways more transparent, it facilitates a better understanding of their functionality, which in turn has implications for their real-world applications. In the domain of behavioral analysis, the research advances the state of the art. One of the main focuses is on human activity recognition, a pivotal problem in various domains including healthcare, surveillance, and human-computer interaction. A suite of innovative deep learning models emerges, each with the capacity to recognize and classify human activities. These models represent a significant step forward in the field of behavioral analysis, as they demonstrate the potential to classify human actions with a reliable level of accuracy. Moreover, always in this field, numerous clinical applications are presented where deep learning models and artificial intelligence can impact the difficult world of neuro-developmental disorders, being of great help to doctors, psychiatrists, and psychologists for the detection of unfortunate conditions such as autism spectrum disorder, attention deficit and hyperactivity disorder and several other related and common conditions. In summation, this doctoral thesis stands as an exemplar of scientific inquiry. It deftly navigates the intricate landscape of deep learning models, employing them as tools for deciphering the enigmas of human emotion and behavior. Through the systematic examination of facial landmarks, the models capture and classify emotions, offering a deeper understanding of human sentiment. In parallel, the thesis shines a spotlight on model transparency, providing a blueprint for explainable AI. In the realm of behavioral analysis, it forges new frontiers with innovative models, raising the bar for the recognition and prediction of human activities and for the detection of clinical conditions. In essence, this thesis serves to analyze the symbiotic relationship between advanced technology and human comprehension, revealing the inner workings of our emotions and actions through the lens of data-driven analysis.

Produzione scientifica

11573/1693992 - 2024 - Bimodal Feature Analysis with Deep Learning for Autism Spectrum Disorder Detection
Colonnese, Federica; Di Luzio, Francesco; Rosato, Antonello; Panella, Massimo - 01a Articolo in rivista
rivista: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS (World Scientific Publishing Company:PO Box 128, Farrer Road, Singapore 912805 Singapore:011 65 6 4665775, EMAIL: journal@wspc.com.sg, INTERNET: http://www.wspc.com.sg, http://www.worldscinet.com, Fax: 011 65 6 4677667) pp. 1-16 - issn: 0129-0657 - wos: WOS:001116423300001 (0) - scopus: 2-s2.0-85179809139 (0)

11573/1710442 - 2024 - Reti neurali applicate all’elaborazione di segnali biomedici per l’analisi comportamentale
Di Luzio, F.; Colonnese, F.; Rosato, A.; Panella, M. - 04d Abstract in atti di convegno
congresso: XXXVIII Riunione Annuale dei Ricercatori di Elettrotecnica (Bari, Italia)
libro: Memorie ET2024 - ()

11573/1691211 - 2023 - Fast convolutional analysis of task-based fMRI data for ADHD detection
Colonnese, F.; Di Luzio, F.; Rosato, A.; Panella, M. - 04b Atto di convegno in volume
congresso: 17th International Work-Conference on Artificial Neural Networks, IWANN 2023 (Ponta Delgada; Portugal)
libro: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - (978-3-031-43077-0; 978-3-031-43078-7)

11573/1691212 - 2023 - A deep neural network for G-quadruplexes binding proteins classification
Di Luzio, F.; Paiardini, A.; Colonnese, F.; Rosato, A.; Panella, M. - 04b Atto di convegno in volume
congresso: 17th International Work-Conference on Artificial Neural Networks, IWANN 2023 (Ponta Delgada; Portugal)
libro: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - (978-3-031-43084-8; 978-3-031-43085-5)

11573/1664070 - 2023 - A randomized deep neural network for emotion recognition with landmarks detection
Di Luzio, F.; Rosato, A.; Panella, M. - 01a Articolo in rivista
rivista: BIOMEDICAL SIGNAL PROCESSING AND CONTROL (Oxford : Elsevier, 2006-) pp. 1-9 - issn: 1746-8094 - wos: WOS:000932971900002 (5) - scopus: 2-s2.0-85144009774 (10)

11573/1710436 - 2022 - Reti neurali randomizzate per la predizione di serie energetiche
Di Luzio, F.; Rosato, A.; Panella, M. - 04d Abstract in atti di convegno
congresso: XXXVI Riunione Annuale dei Ricercatori di Elettrotecnica (Ancona, Italia)
libro: Memorie ET2022 - ()

11573/1655501 - 2022 - A price-aware dynamic decision system in energy communities
Di Luzio, F.; Succetti, F.; Rosato, A.; Araneo, R.; Panella, M. - 04b Atto di convegno in volume
congresso: 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022 (Prague; Czech Republic)
libro: 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2022 - (978-1-6654-8537-1)

11573/1683484 - 2022 - Detection of Autism Spectrum Disorder by a Fast Deep Neural Network
Di Luzio, Francesco; Colonnese, Federica; Rosato, Antonello; Panella, Massimo - 04b Atto di convegno in volume
congresso: International Conference on Applied Intelligence and Informatics - All 2022 (Reggio Calabria, Italy)
libro: AII 2022: Applied Intelligence and Informatics - (978-3-031-24801-6; 978-3-031-24800-9)

11573/1657000 - 2022 - A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition
Succetti, F.; Rosato, A.; Di Luzio, F.; Ceschini, A.; Panella, M. - 01a Articolo in rivista
rivista: ELECTROMAGNETIC WAVES (EMW Publishing:PO Box 425517:Cambridge, MA 02142:(617)354-9597, INTERNET: http://www.emwave.com, Fax: (617)547-3137) pp. 127-141 - issn: 1070-4698 - wos: WOS:000824736600001 (9) - scopus: 2-s2.0-85134012948 (10)

11573/1630054 - 2021 - Deep Neural Networks for Electric Energy Theft and Anomaly Detection in the Distribution Grid
Ceschini, A.; Rosato, A.; Succetti, F.; Di Luzio, F.; Mitolo, M.; Araneo, R.; Panella, M. - 04b Atto di convegno in volume
congresso: 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 (Bari; Italy)
libro: 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings - (978-1-6654-3613-7)

11573/1580314 - 2021 - A blockwise embedding for multi-day-ahead prediction of energy time series by randomized deep neural networks
Di Luzio, F.; Rosato, A.; Succetti, F.; Panella, M. - 04b Atto di convegno in volume
congresso: 2021 International Joint Conference on Neural Networks, IJCNN 2021 (Shenzhen; China - Virtual)
libro: Proceedings of the International Joint Conference on Neural Networks - (978-0-7381-3366-9)

11573/1630052 - 2021 - Multivariate Prediction of Energy Time Series by Autoencoded LSTM Networks
Succetti, F.; Di Luzio, F.; Ceschini, A.; Rosato, A.; Araneo, R.; Panella, M. - 04b Atto di convegno in volume
congresso: 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 (Bari; Italy)
libro: 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings - (978-1-6654-3613-7)

11573/1566169 - 2021 - Time series prediction with autoencoding LSTM networks
Succetti, Federico; Ceschini, Andrea; Di Luzio, Francesco; Rosato, Antonello; Panella, Massimo - 04b Atto di convegno in volume
congresso: 6th International Work-Conference on Artificial Neural Networks, IWANN 2021 (Virtual, Online)
libro: Lecture Notes in Computer Science - (978-3-030-85098-2; 978-3-030-85099-9)

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