Lip feature extraction based on Pulse Coupled Neural Network

Pulse Coupled Neural Network (PCNN) is used to extract lip features in the gray image sequences of visual speech, Time series, Entropy series, Logarithm series, and Standard deviation are considered as the feature vector. Experiments are carried out based on HMM with 4 states and 16 Gaussian mixture components in a small database for speaker-dependent case. Comparing with the traditional feature extracting method by Discrete Cosine Transform (DCT), Experiment results show that feature vector based on PCNN get the higher recognition rates than feature vector based on DCT. The maximum recognition rate improves 7.87% than DCT based lip feature.