Kulkarni Saket S, Reddy Narender P, Hariharan S I
Department of Biomedical Engineering, University of Akron, Akron, OH 44325-0302, USA.
Biomed Eng Online. 2009 Aug 5;8:16. 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.
面部表情在促进人际沟通和互动方面很重要。此外，它们还被用作行为研究和医学康复中的重要工具。基于面部图像的情绪检测技术可能为非侵入性情绪检测提供一种快速且实用的方法。本研究的目的是使用委员会神经网络开发一个基于面部图像的表情分类智能系统。
从面部图像中提取几个面部参数，并用于训练几个通用和专用神经网络。基于初步测试，将表现最佳的通用和专用神经网络纳入决策委员会，这些委员会组成了一个集成的委员会神经网络系统。然后使用从未用于训练或初步测试的受试者那里获得的数据对集成的委员会神经网络系统进行评估。
该系统从62名未用于训练或初步测试的受试者的282张图像中正确识别出255张图像中的正确面部表情（占病例的90.43%）。委员会神经网络为基于图像的情绪检测提供了一种潜在工具。
