Enhanced view invariant gait recognition using feature level fusion

In this paper, following the model-free approach for gait image representation, an individual recognition system is developed using the Gait Energy Image (GEI) templates. The GEI templates can easily be obtained from an image sequence of a walking person. Low dimensional feature vectors are extracted from the GEI templates using Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA), followed by the nearest neighbor classification for recognition. Genuine and imposter scores are computed to draw the Receiver Operating Characteristics (ROC). In practical scenarios, the viewing angles of gallery data and probe data may not be the same. To tackle such difficulties, View Transformation Model (VTM) is developed using Singular Value Decomposition (SVD). The gallery data at a different viewing angle are transformed to the viewing angle of probe data using the View Transformation Model. This paper attempts to enhance the overall recognition rate by an efficient method of fusion of the features which are transformed from other viewing angles to that of probe data. Experimental results show that fusion of view transformed features enhances the overall performance of the recognition system.