Facial Expression Recognition Based on HMM and PSO
Zhong Bi-liang · Jisuanji gongcheng · 2008
This paper presents an approach for Hidden Markov Model(HMM) training based on Particle Swarm Optimization(PSO). HMM parameter adjustment rules are induced by using PSO and Quantum-behaved Particle Swarm Optimization(QPSO) separately. Discriminative information contained in the training data is used to improve the performance on HMM effectively and the method is used in facial expression recognition. Facial expression feature vectors are extracted by using Discrete Cosine Transform(DCT). Experimental results show that the new method provides satisfactory recognition performance and is powerful for HMM parameter estimation.