CONF fasel02b-conf/IDIAP Facial Expression Analysis using Shape and Motion Information Extracted by Convolutional Neural Networks Fasel, B. EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-49.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/fasel-rr-01-49 Related documents International IEEE Workshop on Neural Networks for Signal Processing (NNSP 02) 2002 Martigny, Switzerland September 2002 607-616 IDIAP-RR 01-49 In this paper we discuss a neural networks-based face analysis approach that is able to cope with faces subject to pose and lighting variations. Especially head pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. Data-driven shape and motion-based face analysis approaches are introduced that are not only capable of extracting features relevant to a given face analysis task at hand, but are also robust with regard to translation and scale variations. This is achieved by deploying convolutional and time-delayed neural networks, which are either trained for face shape deformation or facial motion analysis. REPORT fasel-RR-01-49/IDIAP Facial Expression Analysis using Shape and Motion Information Extracted by Convolutional Neural Networks Fasel, B. EXTERNAL https://publications.idiap.ch/attachments/reports/2001/rr01-49.pdf PUBLIC Idiap-RR-49-2001 2001 IDIAP Published in the Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (NNSP 2002,',','), Martigny, Switzerland, 2002 In this paper we discuss a neural networks-based face analysis approach that is able to cope with faces subject to pose and lighting variations. Especially head pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. Data-driven shape and motion-based face analysis approaches are introduced that are not only capable of extracting features relevant to a given face analysis task at hand, but are also robust with regard to translation and scale variations. This is achieved by deploying convolutional and time-delayed neural networks, which are either trained for face shape deformation or facial motion analysis.