Accurate eye detection using generalized binary pattern

This paper proposes the eye detection using generalized binary pattern (GBP). The GBP can generate all possible patterns of ordered comparisons within a 3 × 3 neighborhood. Since existing local structure patterns consider all neighboring pixels around a given pixel, the number of possible patterns is fixed and limited to 2ⁿ. However, since the GBP takes the ordered comparisons of some partial neighboring pixels around a given pixel, a total of 502 different types can be generated in 3 × 3 block. So, our proposed GBP generates 19,162 binary patterns at the given pixel. Among the possible binary patterns, we take an effective set of pattern and position by the AdaBoost feature selection algorithm. Experimental results shows that the GBP provides higher eye detection accuracy on the BioID and FERET databases than other existing local structure patterns such as LBP and MCT.