Model Based Analysis of Face Images for Facial Feature Extraction

This paper describes a comprehensive approach to extract a common feature set from the image sequences. We use simple features which are easily extracted from a 3D wireframe model and efficiently used for different applications on a benchmark database. Features verstality is experimented on facial expressions recognition, face reognition and gender classification. We experiment different combinations of the features and find reasonable results with a combined features approach which contain structural, textural and temporal variations. The idea follows in fitting a model to human face images and extracting shape and texture information. We parametrize these extracted information from the image sequences using active appearance model (AAM) approach. We further compute temporal parameters using optical flow to consider local feature variations. Finally we combine these parameters to form a feature vector for all the images in our database. These features are then experimented with ...

• Type: Conference
• Year: 2009
• Where: CAIP
• Authors: Zahid Riaz, Christoph Mayer, Michael Beetz, Bernd Radig
• Keywords: CAIP 2009, Face Image, Facial Expressions, Image Sequences, Pattern Recognition