Facial Expression Recognition based on Independent Component Analysis

As an important part of artificial intelligence and pattern recognition, facial expression recognition has drawn much attention recently and numerous methods have been proposed. Feature extraction is the most important part which directly affects the final recognition results. Independent component analysis (ICA) is a subspace analysis method, which is also a novel statistical technique in signal processing and machine learning that aims at finding linear projections of the data that maximize their mutual independence. In this paper, we introduce the basic theory of ICA algorithm in detail and then present the process of facial expression recognition based on ICA model. Finally, we use PCA and ICA algorithm to extract facial features, and then SVM classifier is used for facial expression recognition. Experimental results show ICA is a real effective facial expression recognition method and the recognition rate based on ICA is greater than based on PCA and 2DPCA.