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Research Article Open Access
Face recognition systems are becoming popular in most of the applications ranging from gaming to surveillance. Conventionally, 2D cameras are used in face recognition applications and hence 2D face recognition algorithms are employed. However, it has become very easy to obtain 3D camera at cheaper cost, now days. It is interesting to see how 3D camera information can be used to overcome the challenges faced by conventional 2D face algorithms. Recently Creative Labs launched Senz3D camera which outputs the RGB and depth images with misalignment in object present in them. To overcome this, we proposed two algorithms in this paper. It is found that these algorithms have improved the recognition performance significantly. We also explored the various feature representations for the fusion of RGB and depth face ROIs. The fusion of RGB and depth ROIs representation is achieved at matching score level with nearest neighbor classifier. It is observed that among all combinations of feature representations for RGB and depth images, two methods, HOG (RGB) +LDA (Depth) and LBP (RGB) +LDA (Depth), have performed better for fusion system.Additionally, the fusion can play more critical role in the situations where light is not present in vicinity by adaptively increasing the fusion weight associated with depth information.