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Face Description with Local Binary Patterns: Application to Face Recognition. Timo Ahonen , Abdenour Hadid and Matti Pietikainen. Overview. Motivation Local Binary Pattern Methodology Application to Face recognition. Motivation. 2-D surface texture is a valuable cue in machine vision
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Face Description with Local Binary Patterns: Application to Face Recognition TimoAhonen, AbdenourHadid and MattiPietikainen
Overview • Motivation • Local Binary Pattern Methodology • Application to Face recognition
Motivation • 2-D surface texture is a valuable cue in machine vision • To develop leading-edge methodology for 2-D texture analysis; • To create basis for new applications of machine vision. • Guiding principles • Computational simplicity for real-time operation • Invariance wrt. illumination changes • Invariance wrt. spatial rotation of objects
Description of Local image texture • Texture at gcis modeled using a local neighborhood of radius R, which is sampled at P (8 in the example) points: • Let’s define texture T as the joint distribution of the gray levels gc and gp (p=0,…,P-1): • T = t(gc,g0 ,…,gP-1 )
Description of Local image texture (cont.) • Without losing information, we can subtract gcfrom gp : T = t(gc, g0-gc,…, gP-1-gc) • Assuming that gcis independent of gp-gc, we can factorize above: T ~ t(gc) t( g0-gc ,…, gP-1-gc) • t(gc) describes the overall luminance of the image, which is unrelated to local image texture, hence we ignore it: T ~ t( g0-gc ,…, gP-1-gc) Above expression is invariant wrt. Gray scale shifts
LBP: Local Binary Pattern • Invariance wrt. to any monotonic transformation of the gray scale is achieved by considering the signs of the difference: T ~ t( s(g0-gc),…, s(gP-1-gc)) Where • Above is transformed into a unique P-bit pattern code by assigning binomial coefficient 2p to each s(gp-gc):
LBP: Local Binary Pattern (cont.) • LBPP,Rencodes simple binary microstructures into P-bit number: • LBPP,Rprovides less information than signed difference p8 but: • invariant wrt. To any monotonic transformation of the gray scale • Vector quantization not needed • Computational simplicity
LBP: Example • Local Binary Pattern (LBP) is a texture descriptor which codifies local primitives (such as curved edges, spots, flat areas, etc.) into a feature histogram.
Uniform Pattern Heuristic hypothesis • Certain local binary patterns are fundamental properties of texture, providing a vast majority, sometimes overall 90%, of all 3x3 patterns in the observed textures: • Define the concept of ‘uniform’ patterns, which have a limited number of spatial transitions • Use only uniform patterns • Exclude ‘nonuniform’ patterns of high angular frequency (they provide statistically unreliable information)