A Method of Expression Feature Extraction Using Optimized ICA

A combined method of expression feature extraction with particle swarm optimization (PSO) and independent component analysis (ICA) is proposed. The basic ICA algorithm is used to derive the independent base vector from the expression images. To decrease the computing complexity, the dimension of the expression image is reduced, and then PSO algorithm is applied to process expression data set to get the best optimal solution set. Finally, hidden Markov model is used to validate the correctness and validity of the algorithm. The experiments in the expression database show faster way of expression features extraction based on correct rate of expression recognition.