An Efficient Gait Recognition System For Human Identification Using Modified ICA

Biometric systems are becoming increasingly important, as they provide more reliable and efficient means of identity verification. Human identification at a distance has recently gained enormous interest among computer vision researchers. Gait recognition aims essentially to address this problem by recognising people based on the way they walk. In this paper, we propose an efficient self-similarity based gait recognition system for human identification using modified Independent Component Analysis (MICA). Initially the background modelling is done from a video sequence. Subsequently, the moving foreground objects in the individual image frames are segmented using the background subtraction algorithm. Then, the morphological skeleton operator is used to track the moving silhouettes of a walking figure. The MICA based on eigenspace transformation is then trained using the sequence of silhouette images. Finally, when a video sequence is fed, the proposed system recognizes the gait features and thereby humans, based on self-similarity measure. The proposed system is evaluated using gait databases and the experimentation on outdoor video sequences demonstrates that the proposed algorithm achieves a pleasing recognition performance.