LLBPLow-Level Business Process
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From Table 1, it can be evidenced that LLBP has outperformed other texture-based methods.
From the experiments, LLBP can be considered as the best texture-based technique among several texture-based techniques for face authentication.
In addition, the LLBP operator considers only the horizontal and vertical orientations in an image, and in the square window (Fig.
Inspired by the good power of Gabor filter [2, 9] in capturing specific texture characteristics from any orientation of an image, the proposed GLLBP extends LLBP for line pattern extraction into arbitrary orientation.
Table 1 shows GLLBP values of line patterns according to six different orientations for the same sub-block as the one used for showing how to obtain LLBP values in Fig.
The other is to use a histogram to represent the micro patterns in the LBP or LLBP image.
First, it clearly demonstrates that the method using average gray value for thresholding has better matching accuracy than using a center point value in LLBP [15].
As mentioned, the effective information is not fully utilized in LLBP. To solve this problem, the matching scores from GLLBP components with the most discriminative ability are fused to generate the last matching score.
In order to evaluate the effectiveness of the proposed method, similar methods using oriented features and local features such as LBP [12], LLBP [15], local directional code (LDC) [23], Gabor filter [9], and steerable filter [24] are implemented for comparison.
LLBP-15 devotes the LLBP codes at 0[degrees] and 90[degrees] are combined together when the length of line pattern is 15.
Compared with the proposed method, LDC-00 and LDC-45 only extract the local directional codes from an image, while the LLBP codes extracted at 0 and 90 degrees cannot exploit the most discriminative features.