This paper presents a new Feature Local Binary Patterns (FLBP) 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 I, 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, a FLBP representation of the given image I can be formed. Rather than the traditional LBP which 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. We apply the FLBP to eye detection. The experimental results using the BioID database show that the FLBP method significantly improves upon the LBP method. The FLBP method displays superior representational power and flexibility to the LBP method due to the introduction of feature pixels as well as its parameters.