  Facial expressions are an integral part of human cognition and communication,
and can be applied in various real life applications. A vital precursor to
accurate expression recognition is feature extraction. In this paper, we
propose SenTion: A framework for sensing facial expressions. We propose a novel
person independent and scale invariant method of extracting Inter Vector Angles
(IVA) as geometric features, which proves to be robust and reliable across
databases. SenTion employs a novel framework of combining geometric (IVA's) and
appearance based features (Histogram of Gradients) to create a hybrid model,
that achieves state of the art recognition accuracy. We evaluate the
performance of SenTion on two famous face expression data set, namely: CK+ and
JAFFE; and subsequently evaluate the viability of facial expression systems by
a user study. Extensive experiments showed that SenTion framework yielded
dramatic improvements in facial expression recognition and could be employed in
real-world applications with low resolution imaging and minimal computational
resources in real-time, achieving 15-18 fps on a 2.4 GHz CPU with no GPU.
