Gait recognition using occluded data

Gait is an attractive biometrics for use in monitoring and surveillance applications. In such settings, occlusion is common and may affect recognition. This paper investigates the performance of gait using occluded data. To reconstruct the data, interpolation is applied to the occluded data using the Support Vector Machines for Regression (SVR) framework. Then the Principal Component Analysis (PCA) and Canonical Analysis (CA) are applied to reduce the dimensionality of the reconstructed data and classification. Comparison is made between the recognition accuracy rates obtained using the occluded and visible data of the same subject.