Biometric gait identification based on a multilayer perceptron

Abstract

In this study, we propose a novel approach for biometric gait identification. We designed a multilayered back-propagation algorithm-based artificial neural network for gait pattern classification and we compared the results obtained with those produced using the k-means and k-nearest neighbor algorithms. A novel aspect of our feature extraction procedure was the use of a kernel-based principal components analysis because the captured real-time data exhibited significant nonlinearity. The gait data were classified into four classes: normal, crouch-2, crouch-3, and crouch-4. The proposed method achieved gait identification with very good activity recognition accuracy (ARA). The experimental results demonstrated that the proposed methodology could recognize different activities accurately in outdoor and indoor environments, while maintaining a high ARA. The identification of disordered or abnormal gait patterns was the fundamental aim of this study. Thus, we propose a method for the early detection of abnormal gait patterns, which can provide warnings about the potential development of diseases related to human walking. Furthermore, this gait-based biometric identification method can be utilized in the detection of gender, age, race, and for authentication purposes.