Appearance based object recognition using two-dimensional optimal feature transform

This paper proposes a new method of feature extraction called two-dimensional optimal feature transform (2D-OFT) useful for appearance based object recognition. The 2D-OFT method provides a better discrimination power between classes by maximizing the distance between class centers and minimizing the intra-class distance. We first argue that the proposed 2D-OFT method works in the row direction of images and subsequently we propose an alternate 2D-OFT which works in the column direction of images. To straighten out the problem of massive memory requirements of the 2D-OFT method and as well the alternate 2D-OFT method, we introduce bi-projection 2D-OFT. The introduced bi-projection 2D-OFT method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA/2D-FLD/Generalized 2D-PCA method, and the same has been revealed through extensive experimentation conducted on COIL-20 dataset and AT&T face dataset