Automated Facial Expression Recognition System

Abstract

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 MellonUniversity. 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.

Surface Electromyography-Based Facial Expression Recognition in Bi-Polar Configuration

“…It is a common standard to systematically categorize the physical expression of emotions. Later, systems based on Images and video processing were introduced and applied (Cohen et al, 2003;Abboud and Davoine, 2004;Ryan et al, 2010;Liejun et al, 2009). It is a popular and easy one with very low costs and acceptable recognition rates (Buenaposada et al, 2008).…”

Entity-Based Opinion Mining from Text and Multimedia

“…Once a model is fitted to an image, the parameters can then be used as input to an expression classifier that can determine an expression label for the face. More specifically, the muscular movements encoded by FACS map to combinations of parameters in the face model, so a classifier can be potentially trained to recognise these actions [34,35,36]. Figure 1 shows a screenshot of our experimental CLM-based expression recognition system which has been trained to recognise FACS AUs in a Fig.…”

Automatic Facial Expression Recognition

“…Their systems during the last decade have utilized Active Appearance Models for face registration, thus allowing them to estimate the locations of 68 different feature points which are then classified into an expression. It is noteworthy, however, that their group has recently augmented the feature vector of their expression recognizer with appearance-based features: Given an AAM fitted to the face, the feature vector then consists of the non-rigid shape parameters (geometric features), concatenated with the pixel values of the face after removing teh non-rigid shape variation by warping it back onto a canonical face model [53,2]. The combined feature vector is then classified by a support vector machine.…”

“…Efforts to automate the Facial Actions Coding System started at the end of the 20th century [11] and have now become the focus of academic teams [29,3,53,10,48,26] and commercial ventures.…”