Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award. He leads the Human Pose Recovery and Behavior Analysis Group. He is an associate professor at the Dept. of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at UOC, Aalborg, and Dalhousie universities. He has been visiting professor at TU Delft and Aalborg Univ. He is member of the Computer Vision Center. He is series editor of Springer Series on Challenges in Machine Learning. He is vice-president of ChaLearn Challenges in Machine Learning, leading Looking at People events. He is co-founder of Care Respite company. He is member of AERFAI Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, INNS, and Chair of IAPR TC-12: Multimedia and visual information systems.

Research interests

Affective computing, computer vision, and machine learning, with special interest in characterizing people: personality and psychological profile computing. The main objective of his research is to reproduce from a computational perspective the human visual system so that intelligent systems can, ethically, provide with automatic interpretations of human behaviour from visual data coming from non-invasive environments/setups. This research is grounded on multi-disciplinarity, combining knowledge from computing, mathematics, artificial intelligence (computer vision and machine learning) and psychology to translate psychological theories into computational models. As a result he created pioneer models, mainly based on deep learning, that allow to detect and analyse people in visual data: from human posture estimation, facial expression analysis, action/gesture/interaction recognition, to the automatic detection of feelings and personality profiles.

Keywords

Computer Vision, Machine Learning, Human Posture Analysis, Human behavior Analysis, Action and Gesture Recognition, Convolutional Neural Networks, Emotion Recognition, Affective Computing, Personality Computing