Scaling, rotation, and translation invariant image recognition using competing multiple subspaces

We propose a tolerant object recognition system under a combination of various transformations of an object image. The system realizes invariant recognition by re-normalizing the image with multiple units each of which is assigned to the individual transformation. The re-normalization process is an iterative procedure in which only the most accurate unit re-normalizes the image´s every iteration. To implement the re-normalization units, we utilize a kernel-based non-linear subspace model. In the model, projection of the image to the subspace represents the amount of transformation in the manner of the population coding. In addition, the accuracy of the representation can be known as distance between the image and the subspace. The system is applied to face detection from snapshots to show significant robustness under scaling, rotation, and translation.