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• Over-Complete Local Binary Patterns (OCLBP):cite-ref-9[9] OCLBP is a variant of LBP that has been shown to improve the overall performance on face verification. Unlike LBP, OCLBP adopts overlapping to adjacent blocks. Formally, the configuration of OCLBP is denoted as S : (a, b, v, h, p, r): an image is divided into a×b blocks with vertical overlap of v and horizontal overlap of h, and then uniform patterns LBP(u2,p,r) are extracted from all the blocks. Moreover, OCLBP is composed of several different configurations. For example, in their original paper, the authors used three configurations: S : (10,10,12,12,8,1),(14,14,12,12,8,2),(18,18,12,12,8,3). The three configurations consider three block sizes: 10×10, 14×14, 18×18, and half overlap rates along the vertical and horizontal directions. These configurations are concatenated to form a 40877 dimensional feature vector for an image of size 150x80.

• Transition Local Binary Patterns(tLBP):cite-ref-10[10] binary value of transition coded LBP is composed of neighbor pixel comparisons clockwise direction for all pixels except the central.

• Direction coded Local Binary Patterns(dLBP): the dLBP encodes the intensity variation along the four basic directions through the central pixel in two bits.

• Multi-block LBP: the image is divided into many blocks, a LBP histogram is calculated for every block and concatenated as the final histogram.

• Volume Local Binary Pattern(VLBP):cite-ref-11[11] VLBP looks at dynamic texture as a set of volumes in the (X,Y,T) space where X and Y denote the spatial coordinates and T denotes the frame index. The neighborhood of a pixel is thus defined in three dimensional space, and volume textons can be extracted into histograms.

• RGB-LBP: This operator is obtained by computing LBP over all three channels of the RGB color space independently, and then concatenating the results together.

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