Facial expression (mood) recognition from facial images using committee neural networks

• Published in: Biomedical engineering online Vol. 8; no. 1; p. 16
• Main Authors: Kulkarni, Saket S, Reddy, Narender P, Hariharan, S I
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 05-08-2009; BioMed Central; BMC
• Subjects: Accuracy; Affect - physiology; Algorithms; Artificial Intelligence; Committees; Data processing; Emotions; Facial Expression; Female; Humans; Identification and classification; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Male; Mood (Psychology); Neural networks; Neural Networks (Computer); Pattern Recognition, Automated - methods; Principal components analysis; Reproducibility of Results; Sensitivity and Specificity; Studies
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/1475-925X-8-16

Facial 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.
Several 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.
The 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.