Extended Set of Local Binary Patterns for Rapid Object Detection

The paper presents two new encoding schemes for representation of the intensity function in a local neighborhood. The encoding produces binary codes, which are complementary to the standard local binary patterns (LBPs). Both new schemes preserve an important property of the LBP, the invariance to monotonic transformations of the intensity. Moreover, one of the schemes possesses invariance to gray scale inversion. The utility of the new encodings is demonstrated in the framework of AdaBoost learning. The new LBP encoding schemes were tested on the face detection, car detection and gender recognition problems using the CMU-MIT frontal face dataset, the UIUC Car dataset and the FERET dataset respectively. Experimental results show that the proposed encoding methods improve both the accuracy and the speed of the final classifier. In all tested tasks, a combination of the encoding schemes outperforms the original one. No LBP encoding scheme dominates, the relative importance of the schemes is problem-specific.