Gait as a biometric with the unique capability to recognize people at a distance is subject to increasing interest. A gait sequence contains static and dynamic components from the walking way. It is pivotal to integrate them to improve the performance of gait recognition. Initiated from the idea of integration

a moment invariants based scheme for gait recognition is proposed in the paper

taking the magnitudes of the Fourier series coefficients representing moment invariants of gaits as features for identification. The moment invariants describe the static components during the walk

whereas dynamic components are contained in the coefficients extracted according to the whole gait sequence. So firstly

the moment invariants of each frame are computed. Secondly

the moment invariants of humans silhouettes are represented with Fourier series

the Fourier coefficients of which are obtained using a genetic algorithm. Thirdly

the magnitudes of the coefficients are generated as vectors to classify the subjects

which are identified by the kNN classifier. The recognition results of four kinds of gaits in the CMU gait database show that the proposed scheme has a correct recognition rate of more than 80% using a single moment and beyond 90% using jointed moments. Moreover

the scheme is also robust to partial occlusion. The experimental results and performance analysis indicate that the scheme is effective as it integrates static and dynamic components for identification.