Gait Recognition Based on Embedded Hidden Markov Model

A gait recognition algorithm based on embedded hidden Markov model(e-HMM) is proposed for the problem of extracting features efficiently from a sequence of gait frames.First,body silhouette extraction is achieved by background subtraction method.Gait period is calculated through analyzing the silhouette-width-vector autocorrelation and the mean gait energy image(GEI) is obtained.Then,spatial feature information of the mean GEI is obtained by two dimensional discrete cosine transform(2D-DCT).The mean GEI observation block is transformed into observation vector and gait recognition is performed.In the end,the nearest-neighbor method is adopted to confirm the proposed algorithm using two different gait databases.Experimental results show that the algorithm has higher recognition performance.