An appearance based approach for gait identification using infrared imaging

Recognition using gait is a behavioral biometric technique which is based on the idea that no two persons in the world can have same walking style on feet. Gait refers to a particular way or manner of walking on feet. People can be monitored and identified at a distance without knowledge to subject. This paper gait recognition system, which is insensitive to view, clothing and lightning conditions. In the proposed work, an infrared camera is used to capture videos of moving objects. After that background subtraction is performed i.e. foreground objects (moving objects) like human, vehicles and animals are extracted by estimating background information. This is achieved using Gaussian Mixture Model (GMM) followed by morphological median filtering operation to remove noise in the background subtracted image. A classification metric is used to separate human being from other foreground objects. Shape and boundary information is used in the moving target classification. Subsequently, width vector of the outer contour of binary silhouette and the MPEG-7 Angular Radial Transform coefficients are taken as the feature vector. Binary silhouette obtained is used to produce gait features of a person. Independent Component Analysis (ICA) applied to reduce dimensionality of the features vectors. These feature vectors used to train Support Vector Machine (SVM) for recognition of some individual. Length of gait cycle, maximum feet and hands distance, contour height, center of mass and color are taken as key feature. Proposed approach is tested over a self created database of thirteen different people with different resolution conditions. In indoor environment an average recognition percentage upto 97.73% and for outdoor conditions recognition is upto 86.28% (daylight) and 78.32% recognition at night hours is observed. Use of infrared cameras makes it quite advantageous to be used at night.