Automatic extraction and description of human gait models for recognition purposes

Abstract

Using gait as a biometric is of emerging interest. We describe a new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process. This is possible by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Simulation results highlight potential performance benefits in the presence of noise. Classification uses the k-nearest neighbour rule applied to the Fourier components of the motion of the upper leg. Experimental analysis demonstrates that an improved classification rate is given by the phase-weighted Fourier magnitude information over the use of the magnitude information alone. The improved classification capability of the phase-weighted magnitude information is verified using statistical analysis of the separation of clusters in the feature space. Furthermore, the technique is shown to be able to handle high levels of occlusion, which is of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.

Gait recognition based on shape and motion analysis of silhouette contours

“…With initial contact ( Fig. 2(a)) as the first phase of a gait period, PWMS provides the greatest variability between subjects, as it conveys additional information about temporal deformation of the sequence of shapes together with its frequency contents [49]. PWMS are represented as a o × k matrix, where o represent the ten phases and k = T/2, i.e., 128.…”

Active Shape Model-Based Gait Recognition Using Infrared Images

“…Moving Shape Model spatiotemporal pattern [6]; Principal Components Analysis(PCA) [7] shape of motion [17]; PCA + Canonical Analysis [18] single oscillator [31] Since 2001…”

Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach

“…By contrast, articulated-model based methods match an explicit volumetric model to image sequences, particularly in multi-views [39][40][41][42], where motion and stereo measurement of body segments is feasible, accurate and robust. In this sense, many studies, as in the field of gait recognition, combine motion-based recognition with a model-based approach, to assist high fidelity feature detection from images [28,42,43]. For example, Ning et al [42] employed a simplified human model with enhanced motion constraints for efficient tracking and recognition.…”