Integral Local Binary Patterns: A Novel Approach Suitable for Texture-Based Object Detection Tasks

This work is concerned with the proposition and empirical evaluation of a new feature extraction approach that combines two existing image descriptors, Integral Histograms and Local Binary Patterns (LBP), to achieve a representation that exhibits relevant properties to object detection tasks (such as face detection): fast constant time processing, rotation, and scale invariance. This novel approach is called the Integral Local Binary Patterns (INTLBP), which is based on an existing method for calculating Integral Histograms from LBP images. This paper empirically demonstrates the properties of INTLBP in a scenario of texture extraction for face/non-face classification. Experiments have shown that the new representation added robustness to scale variations in the test images - the proposed approach achieved a mean classification rate 92% higher than the standard Rotation Invariant LBP approach, when testing over images with scales different from the ones used for training. Moreover, the INTLBP dramatically reduced the required processing time when searching patterns in a face detection task.