关键词: 身份识别, 隐马尔可夫模型（HMM）, 区域划分, 加速度传感器, 峰值点连线斜率, 组合分类器

Abstract: To improve the smart phone built-in acceleration sensor personnel recognition rate, a method of combinatorial classifier recognition based on information segmentation is proposed. Firstly, according to the characteristics of pedestrian walking acceleration, a division method based on HMM（Hidden Markov Model） is proposed. The pedestrian acceleration is divided into two parts：relative dynamic state and steady state. The standard deviation, mean, energy and other characteristics are extracted from the parts. Then, these features which are selected from different walking speed are combined with the slope of the peak point line into a new feature set. Finally, the combination classifier is used to achieve better recognition accuracy. The experimental results show that the recognition of the walking in slow speed of the rate reaches 98.3% and walking in high speed reaches 97.6%. The method of this paper is better than the existing methods of personnel identification.

Key words: gait recognition, Hidden Markov, Model（HMM）, geographic division, acceleration sensor, connection slope of peak point, combined classifier