Spatio-temporal multifeature for facial analysis

Human faces are 3D complex objects consisting of geometrical and appearance variations. They exhibit local and global variations when observed over time. In our daily life communication, human faces are seen in actions conveying a set of information during interaction. Cognitive science explains that human brains are capable of extracting this set of information very efficiently resulting in a better interaction with others. Our goal is to extract a single feature set which represents multiple facial characteristics. This problem is addressed by the analysis of different feature components on facial classifications using a 3D surface model. We propose a unified framework which is capable to extract multiple information from the human faces and at the same time robust against rigid and non-rigid facial deformations. A single feature vector corresponding to a given image is representative of person's identity, facial expressions, gender and age estimation. This feature set is called spatio-temporal multifeature (STMF) extracted from image sequences. An STMF is configured with three different feature components which is tested thoroughly to evidence its validity. The experimental results from four different databases show that this feature set provides high accuracy and at the same time exhibits robustness. The results have been discussed comparatively with different approaches.