Gait is an important biological characteristics in the long distance video surveillance field. Nowadays

almost all gait recognition researcher focus on gait recognition only under one single condition.However

the gait recognition rate rapidly decline in blended conditions

for example when somebody is wearing a coat or carrying a bag. Based on our analysis of the gait timing characteristics during the human movements

we propose a new gait recognition approach that expresses dynamic information and static information by using a dynamic Bayesian networw(DSIF-DBN). The DSIF-DBN contains three levels of states and for every time slice of the DSIF-DBN model is expressed by the fusion of dynamic information and static information . This model can exectly express the timing characteristics of the gait

which are the body posture and the range of motion

as well as other gait rhythmic change characteristics. Experimental result show that the DSIF-DBN model recognizes gait with high rates and good robustness to noise and lost of information. The DSIF-DBN model can fuse the dynamic information as well as static information and can greatly reduce the impact of gait recognition rates when somebody is wearing a coat or carrying a bag.