SenTion: A framework for Sensing Facial Expressions
This addresses the problem of accurate, real-time facial expression recognition for applications like human-computer interaction, with incremental improvements in feature extraction and hybrid modeling.
The paper tackles facial expression recognition by proposing SenTion, a hybrid framework combining geometric (Inter Vector Angles) and appearance-based (Histogram of Gradients) features, achieving state-of-the-art accuracy on CK+ and JAFFE datasets and real-time performance of 15-18 fps on a CPU.
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.