Lip Feature Extraction based on Pulse Coupled Neural Network

摘要：

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.

关键词： feature vector PCNN entropy sequence logarithmic sequence time series standard variance sequence normalized DCT coefficients Hidden Markov Model

作者: Mengjun Wang Xiangling Wang Gang Li

作者单位: School of Information Engineering HeBei University of Technology Tianjin, China School of Precision Instrument and Opto-Electronics Engineering, Tianjin University Tianjin, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 938-941

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）