Abstract
BackgroundFacial expressions are important in facilitating human communication and interactions. Also, they are used as an important tool in behavioural studies and in medical rehabilitation. Facial image based mood detection techniques may provide a fast and practical approach for non-invasive mood detection. The purpose of the present study was to develop an intelligent system for facial image based expression classification using committee neural networks.MethodsSeveral facial parameters were extracted from a facial image and were used to train several generalized and specialized neural networks. Based on initial testing, the best performing generalized and specialized neural networks were recruited into decision making committees which formed an integrated committee neural network system. The integrated committee neural network system was then evaluated using data obtained from subjects not used in training or in initial testing.Results and conclusionThe system correctly identified the correct facial expression in 255 of the 282 images (90.43% of the cases), from 62 subjects not used in training or in initial testing. Committee neural networks offer a potential tool for image based mood detection.
Highlights
Facial expressions are important in facilitating human communication and interactions
A total of 15 parameters consisting of eight real-valued parameters and seven binary parameters were extracted from each facial image
Real valued and binary parameters were extracted from the facial images from 97 subjects (467 images)
Summary
Facial expressions are important in facilitating human communication and interactions. They are used as an important tool in behavioural studies and in medical rehabilitation. Facial expressions and related changes in facial patterns give us information about the emotional state of the person and help to regulate conversations with the person. These expressions help in understanding the overall mood of the person in a better way. The FACS codes different facial movements into Action Units (AU) based on the underlying muscular activity that produces momentary changes in the facial expression. An expression is further recognized by correctly identifying the action unit or combination of action units related to a particular expression
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