  Encoded Local Projections (ELP) is a recently introduced dense sampling image
descriptor which uses projections in small neighbourhoods to construct a
histogram/descriptor for the entire image. ELP has shown to be as accurate as
other state-of-the-art features in searching medical images while being time
and resource efficient. This paper attempts for the first time to utilize ELP
descriptor as primary features for facial recognition and compare the results
with LBP histogram on the Labeled Faces in the Wild dataset. We have evaluated
descriptors by comparing the chi-squared distance of each image descriptor
versus all others as well as training Support Vector Machines (SVM) with each
feature vector. In both cases, the results of ELP were better than LBP in the
same sub-image configuration.
