Gait Recognition Based On Model-Based Methods And Deep Belief Networks

The sensitivity to illumination variations, pose, gender, age, clothing and any another source of changes, can be one of the most important challenges, in gait recognition system. In this paper, we adopt many approaches to extract signatures of human body ( static model) using a model-based method, such as static body parameters, ellipse-fitting and robust shape coding. To reduce the dimension of this features set, a principal component analysis ( PCA) technique is employed. Then, a deep belief networks classifier is used to classify the gait signatures. The performance of the deep belief network ( DBN) is superior to other classifiers such as k-nearest neighbour ( KNN) and dynamic times warping ( DTW). The comparison is performed for viewpoint changes, clothing and carrying conditions. The proposed approach has been validated on the gait database B.