Event Location: Newell Simon Hall 1507
Bio: Xavier Alameda-Pineda received the M.Sc. degree in mathematics and telecommunications engineering from the Universitat Politècnica de Catalunya – BarcelonaTech in 2008 and 2009 respectively, the M.Sc. degree in computer science from the Université Joseph Fourier and Grenoble INP in 2010, and the Ph.D. degree in mathematics/computer science from the Université Joseph Fourier in 2013. He worked towards his Ph.D. degree in the Perception Team, at INRIA Grenoble Rhône-Alpes. He currently holds a postdoctoral position at the Multimodal Human Understanding Group at University of Trento. His research interests are machine learning and signal processing for scene understanding, speaker diaritzation and tracking, sound source separation and behavior analysis.

Abstract: Matrix completion is a generic framework aiming to recover a matrix from a limited number of (possibly noisy) entries. In this content, low-rank regularizers are often imposed so as to find matrix estimators that are robust to noise and outliers. In this talk I will discuss three recent advances on matrix completion, developed to solve three different vision applications. First, coupled matrix completion to solve joint head and body pose estimation. Second, non-linear matrix completion to recognize emotions from abstract paintings. Third, self-adaptive matrix completion for remote heart-rate estimation from videos.