Robust face recognition algorithm for identifition of disaster victims

We present a robust face recognition algorithm for the identication of occluded, injured and mutilated faces with a limited training set per person. In such cases, the conventional face recognition methods fall short due to specic aspects in the classication. The proposed algorithm involves recursive Principle Component Analysis for reconstruction of aected facial parts, followed by a feature extractor based on Gabor wavelets and uniform multi-scale Local Binary Patterns. As a classier, a Radial Basis Neural Network is employed. In terms of robustness to facial abnormalities, tests show that the proposed algorithm outperforms conventional face recognition algorithms like, the Eigenfaces approach, Local Binary Patterns and the Gabor magnitude method. To mimic real-life conditions in which the algorithm would have to operate, specic databases have been constructed and merged with partial existing databases and jointly compiled. Experiments on these particular databases show that the proposed algorithm achieves recognition rates beyond 95%