双视角下多特征信息融合的步态识别

Gait recognition based on dual-view and multiple feature information fusion

针对步态识别研究中单视角识别率低、多视角算法复杂等问题,开展了双视角下的步态识别研究.考察正面视角人体的轮廓特征和侧面视角人体行走的动态特征,利用多视角步态信息互补性强的特点,分别从正面视角和侧面视角获取步态序列,预处理得到单连通人体轮廓图形,然后对正面视角提取Procrustes均值形状,侧面视角计算动作能量图(AEI)并经二维局部保留映射(2D-LPP)降维,最后将2个视角下的识别结果进行融合从而获得最终的识别结果.在中科院自动化所的DatasetB数据库上进行了实验,获得了较高的识别率,达到了预期的识别效果.

In view of low recognition rate of single-view and complexity of multi-view algorithm, a research was con- ducted examining the gait recognition under dual-view. Current research on the contour characteristic of the human body in frontal view and the dynamic characteristics of human walking in side view was examined using the comple- mentary features of the gait information under multi-view. Also the gait sequences were obtained utilizing the two views respectively, and then preprocessed to obtain simply connected body silhouettes. Next, the Procrustes mean shape was extracted from the fi＇ont view, and the active energy images （AEI） was calculated by side view. Howev- er, each of the AEI was projected to a low-dimensional feature subspace via two-dimensional local preserving pro- jections （2D-LPP）. The final recognition result was obtained by fusing recognition results of two perspectives. The experiments in CASIA dataset（ Dataset B） obtained a high recognition rate and achieved the expected effect of rec- ognition.