Gait recognition based on sparse linear subspace

Abstract

Gait recognition has broad application prospects in intelligent security monitoring. However, due to the variability of human walking states and the complexity of external conditions during sample collection, gait recognition is still facing many challenges. Among them, gait recognition algorithms based on shallow learning are hard to achieve the correct recognition rate required by many applications, while the amount of gait training data cannot meet the needs of model training based on deep learning. To solve the above problem, this paper presents a novel gait recognition scheme based on sparse linear subspace. First, frame-by-frame gait energy images (ffGEIs) are extracted as primary gait features and sparse linear subspace technology is used to represent them for dimension reduction. Second, a new gait classification algorithm based on support vector machine is presented, which adopts Gaussian radial basis function (RBF) kernels to achieve cross-view gait recognition. Finally, the proposed gait recognition approach is evaluated on two open-accessed gait databases to demonstrate its performance.