Study Of ICA' Application In Facial Expression Recognition

In recent years, The study of face recognition. has made very great progress. Some system has realized commercialization. But it does not mean that face recognition technology has been already perfect. At fact, There are still a lot of problems in the field of face recognition. Among those, Automatic is one of the difficult. Automatic facial expression recognition (AFER) which is developed to recognize one of our human special emotional representation-facial expression, has been attracted more and more attention. It is full of challenging and difficult because of the complicated and subtle properties of facial expression. A specific difficulties lies in AFER is its data exists among the high-end data. Independent component analysis algorithm in high-end data processing has its unique advantages. Independent Component Analysis is a newly developed and powerful technique for recovering latent independent sources given only their mixtures. The basic ICA model assumes that sources are linearly mixed and mutually independent. The data analyzed by ICA could originate from many different kinds of application field, include digital images and document database, as well as economic indicators and psychometric measuremenis, etc. Independent component analysis, in its own high-end data processing capability at the same time has brought inevitable increase in complexity of computing, the face recognition system in order to meet the requirements of real-time, the paper will take a fast ICA algorithm (FastICA), and on this basis, to make further improvements to guarantee System for real-time requirements.