Human Gait Recognition Based on Dynamic and Static Features Using Generalized Regression Neural Network

Biometric recognition using the behavioral modality of gait is an emerging research area. This paper describes a method for human gait recognition using generalized regression neural networks. The feature space is composed of a combination of dynamic (time-varying) gait signals and static body-shape parameters, extracted from binary silhouettes obtained after background subtraction from human gait sequences. The inputs to the neural network are obtained by performing discrete cosine transform (DCT) on the feature space, followed by selection of transformed coefficients to construct compact vectors.