Facial gesture recognition using two-channel bio-sensors configuration and fuzzy classifier: A pilot study

Facial gesture recognition has become an important issue in diagnostic, medical and industrial fields. Automatic recognition of facial gestures could be considered as an important factor in human-machine interface applications. Facial gesture recognition based on surface electromyography (SEMG) has been well thought-out in the recent decade. SEMG has accurate rates for facial gesture recognition since it records the electrical potential from facial muscles. This paper presents a method for recognizing 5 different facial gestures based on forehead two-channels bioelectric-signals. The recorded signals were processed in four steps: filtration, feature extraction (RMS), thresholding, and classification. The extracted features were classified into 5 facial gesture classes (rest, smile, frown, rage, and gesturing `notch' by pulling up the eyebrows) by utilizing Fuzzy C-Means (FCM) classifier. Finally 90.8% recognition ratio has been achieved by applying our method on 4 subjects.