Hugo Jair Escalante – författare

This book constitutes the refereed post-conference proceedings of 3 workshops, held at the 24th International Conference on Pattern Recognition, Beijing, China, in August 2018: the Third International Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2018, the 7th International Workshop on Computational Forensics, IWCF 2018, and the International Workshop on Multimedia Information Processing for Personality and Social Networks Analysis, MIPPSNA 2018.The 16 full papers presented in this book were carefully reviewed and selected from 23 submissions. CVAUI Workshop: The analysis of underwater imagery imposes a series of unique challenges, which need to be tackled by the computer vision community in collaboration with biologists and ocean scientists. IWCF Workshop: With the advent of high-end technology, fraudulent efforts are on rise in many areas of our daily life, may it be fake paper documents, forgery in the digital domain or copyright infringement. In solving the related criminal cases use of pattern recognition (PR) principles is also gaining an important place because of their ability in successfully assisting the forensic experts to solve many of such cases. MIPPSNA Workshop: Its goal is to compile the latest research advances on the analysis of multimodal information for facing problems that are not visually obvious, this is, problems for which the sole visual analysis is insufficient to provide a satisfactory solution.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic.

For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain.In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.

This book explores AI methodologies for the implementation of affective states in intelligent learning environments. Divided into four parts, Multimodal Affective Computing: Technologies and Applications in Learning Environments begins with an overview of Affective Computing and Intelligent Learning Environments, from their fundamentals and essential theoretical support up to their fusion and some successful practical applications. The basic concepts of Affective Computing, Machine Learning, and Pattern Recognition in Affective Computing, and Affective Learning Environments are presented in a comprehensive and easy-to-read manner. In the second part, a review on the emerging field of Sentiment Analysis for Learning Environments is introduced, including a systematic descriptive tour through topics such as building resources for sentiment detection, methods for data representation, designing and testing the classification models, and model integration into a learningsystem. The methodologies corresponding to Multimodal Recognition of Learning-Oriented Emotions are presented in the third part of the book, where topics such as building resources for emotion detection, methods for data representation, multimodal recognition systems, and multimodal emotion recognition in learning environments are presented. The fourth and last part of the book is devoted to a wide application field of the combination of methodologies, such as Automatic Personality Recognition, dealing with issues such as building resources for personality recognition, methods for data representation, personality recognition models, and multimodal personality recognition for affective computing.  This book can be very useful not only for beginners who are interested in affective computing and intelligent learning environments, but also for advanced and experts in the practice and developments of the field. It complies an end-to-end treatment on these subjects, especially with educational applications, making it easy for researchers and students to get on track with fundamentals, established methodologies, conventional evaluation protocols, and the latest progress on these subjects.

This book explores AI methodologies for the implementation of affective states in intelligent learning environments. Divided into four parts, Multimodal Affective Computing: Technologies and Applications in Learning Environments begins with an overview of Affective Computing and Intelligent Learning Environments, from their fundamentals and essential theoretical support up to their fusion and some successful practical applications. The basic concepts of Affective Computing, Machine Learning, and Pattern Recognition in Affective Computing, and Affective Learning Environments are presented in a comprehensive and easy-to-read manner. In the second part, a review on the emerging field of Sentiment Analysis for Learning Environments is introduced, including a systematic descriptive tour through topics such as building resources for sentiment detection, methods for data representation, designing and testing the classification models, and model integration into a learningsystem. The methodologies corresponding to Multimodal Recognition of Learning-Oriented Emotions are presented in the third part of the book, where topics such as building resources for emotion detection, methods for data representation, multimodal recognition systems, and multimodal emotion recognition in learning environments are presented. The fourth and last part of the book is devoted to a wide application field of the combination of methodologies, such as Automatic Personality Recognition, dealing with issues such as building resources for personality recognition, methods for data representation, personality recognition models, and multimodal personality recognition for affective computing.  This book can be very useful not only for beginners who are interested in affective computing and intelligent learning environments, but also for advanced and experts in the practice and developments of the field. It complies an end-to-end treatment on these subjects, especially with educational applications, making it easy for researchers and students to get on track with fundamentals, established methodologies, conventional evaluation protocols, and the latest progress on these subjects.

This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems.

This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems.

This book constitutes the refereed proceedings of the 15 Ibero-American Conference on Artificial Intelligence, IBERAMIA 2016, held in San José, Costa Rica, in November 2016. The papers are organized in the following topical sections: knowledge engineering, knowledge representation and probabilistic reasoning;