Automated Facial Expression Recognition System

Heightened concerns about the treatment of individuals during interviews and interrogations have stimulated efforts to develop “non-intrusive” technologies for rapidly assessing the credibility of statements by individuals in a variety of sensitive environments. Methods or processes that have the potential to precisely focus investigative resources will advance operational excellence and improve investigative capabilities. Facial expressions have the ability to communicate emotion and regulate interpersonal behavior. Over the past 30 years, scientists have developed human-observer based methods that can be used to classify and correlate facial expressions with human emotion. However, these methods have proven to be labor intensive, qualitative, and difficult to standardize. The Facial Action Coding System (FACS) developed by Paul Ekman and Wallace V. Friesen is the most widely used and validated method for measuring and describing facial behaviors. The Automated Facial Expression Recognition System (AFERS) automates the manual practice of FACS, leveraging the research and technology behind the CMU/PITT Automated Facial Image Analysis System (AFA) system developed by Dr. Jeffery Cohn and his colleagues at the Robotics Institute of Carnegie Mellon University. This portable, near real-time system will detect the seven universal expressions of emotion (figure 1), providing investigators with indicators of the presence of deception during the interview process. In addition, the system will include features such as full video support, snapshot generation, and case management utilities, enabling users to re-evaluate interviews in detail at a later date.

[1]  D. Keltner.  Facial Expressions of Emotion and Personality , 1996 .

[2]  Timothy F. Cootes,et al.  Interpreting face images using active appearance models , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[3]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[4]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Jennifer S. Beer,et al.  Facial expression of emotion. , 2003 .

[6]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[7]  Ralph Gross,et al.  Generic vs. person specific active appearance models , 2005, Image Vis. Comput..

[8]  Maja Pantic,et al.  Web-based database for facial expression analysis , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[9]  Timothy F. Cootes,et al.  Feature Detection and Tracking with Constrained Local Models , 2006, BMVC.

[10]  Simon Baker,et al.  2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting , 2007, International Journal of Computer Vision.

[11]  Klaus R. Scherer,et al.  Using Actor Portrayals to Systematically Study Multimodal Emotion Expression: The GEMEP Corpus , 2007, ACII.

[12]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[13]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[14]  Sridha Sridharan,et al.  Automatically detecting action units from faces of pain: Comparing shape and appearance features , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[15]  Sridha Sridharan,et al.  Efficient constrained local model fitting for non-rigid face alignment , 2009, Image Vis. Comput..

[16]  Tsuhan Chen,et al.  The painful face - Pain expression recognition using active appearance models , 2009, Image Vis. Comput..