GRAB: generalized region assigned to binary
Archana Sapkota, Terrance E. Boult · EURASIP Journal on Image and Video Processing · 2013
Scale is one of the major challenges in recognition problems. For example, a face captured across large distances is considerably harder to recognize than the same face at small distances. Local binary pattern (LBP) and its variants have been successfully used in face detection, recognition, and many other computer vision applications. While LBP features are shown to be discriminative in face recognition, the pixel level description of LBP features is sensitive to the change in scale of the images. In this work, we extend the utility of a generalized variant of LBP feature descriptor called generalized region assigned to binary (GRAB), previously introduced in an article below, and show that it handles the challenges due to scale. The original LBP operator in another article is defined with respect to the surrounding pixel values while the GRAB operator is defined with respect to overlapping surrounding regions. This gives more general description and flexibility in choosing the right operator depending on the varying imaging conditions such as scale variations. We also propose a way to automatically select the scale of the GRAB operator (size of neighborhood). A pyramid of multi-scale GRAB operators is constructed, and the operator at each scale is applied to an image. Selection of operator’s scale is performed based on the number of stable pixels at different levels of the multi-scale pyramid. The stable pixels are defined to be the pixels in the images for which the GRAB value remains the same even as the GRAB operator scale changes. In addition to the experiments in the former article, we apply basic LBP, Liao et al.’s multi-scale block (MB)-LBP, and GRAB operator on face recognition across multiple scales and demonstrate that GRAB significantly outperforms the basic LBP and is more stable compared to MB-LBP in cases of reduced scale on a subsets of a well-known published database of labeled faces in the wild (LFW). We also perform experiments on the standard LFW database using strict LFW protocol and show the improved performance of GRAB descriptor compared to LBP and Gabor descriptors.