CNN-based object recognition with deformable grids and multiple-feature image representation

The following paper presents a new direction that opens for deformable grid-based object recognition methods, due to introduction of their efficient, parallel implementations. A substantial increase in object recognition performance can be expected when several different features are used to build a class prototype. This would imply extending complexity of image analysis, through an application of several image characteristics in image-model matching. To make such an approach computationally feasible, a CNN is considered as ultra-fast tool for performing grid-matching process. Sample task of face recognition, which is well-suited for being tackled with deformable grids, is used to evaluate a performance of the proposed approach, yielding an expected increase in correct classification rate.