Alberto Del Bimbo

Marco Bertini

Hugo Jair Escalante

Rita Cucchiara

Giovanni Maria Farinella

Stan Sclaroff

Tao Mei

Roberto Vezzani

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. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.

Virtual Event, January 10–15, 2021, Proceedings, Part VI

Pattern Recognition. ICPR International Workshops and Challenges

Virtual Event, January 10–15, 2021, Proceedings, Part V

Sergio Escalera

Stan Z. Li

Jun Wan

Guodong Guo

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.

Multi-Modal Face Presentation Attack Detection

Virtual Event, January 10–15, 2021, Proceedings, Part III

Virtual Event, January 10–15, 2021, Proceedings, Part II

Manuel Montes y Gómez

Alberto Segura

Juan de Dios Murillo

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 34 papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in the following topical sections: knowledge engineering, knowledge representation and probabilistic reasoning; agent technology and multi-agent systems; planning and scheduling; natural language processing; machine learning; big data, knowledge discovery and data mining; computer vision and pattern recognition; computational intelligence soft computing; AI in education, affective computing, and human-computer interaction.

15th Ibero-American Conference on AI, San José, Costa Rica, November 23-25, 2016, Proceedings

Advances in Artificial Intelligence - IBERAMIA 2016

Yağmur Güçlütürk

Isabelle Guyon

Umut Güçlü

Marcel van Gerven

Xavier Baró

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

Explainable and Interpretable Models in Computer Vision and Machine Learning

Hugo Jair Escalante

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