Feature local binary patterns with application to eye detection

Abstract

This paper presents a new Feature Local Binary Patterns (FLBP) method that encodes the information of both local texture and features. The features are broadly defined by, for example, the edges, the Gabor wavelet features, the color features, etc. Specifically, a binary image is first derived by extracting feature pixels from a given image, and then a distance vector field is obtained by computing the distance vector between each pixel and its nearest feature pixel defined in the binary image. Based on the distance vector field and the FLBP parameters, the FLBP representation of the given image can be formed. In contrast to the original LBP that only compares a pixel with the pixels in its own neighborhood; the FLBP can compare a pixel with the pixels in its own neighborhood as well as in other neighborhoods. The experimental results on eye detection using the BioID and FERET databases show the feasibility of our FLBP method. In particular, first, the FLBP method significantly improves upon the LBP method in terms of both eye detection rate and eye center localization accuracy. Second, we present a new feature pixel extraction method—the LBP with Relative Bias Thresholding (LRBT) method. The new LRBT method helps improve the FLBP eye detection performance when compared with other feature pixel extraction methods. Third, the FLBP method displays superior representational power and flexibility to the LBP method due to the introduction of the feature pixels as well as the FLBP parameters. Finally, in comparison with some state-of-the-art methods, our FLBP method achieves the highest accuracy of eye center localization.