Best result on the LFW face recognition benchmark, at the Faces in Real-Life Images workshop, 2008
Recent methods for learning similarity between images have presented impressive results in the problem of pair matching (same/notsame classification) of face images. In this paper we explore how well this performance carries over to the related task of multi-option face identification, specifically on the Labeled Faces in the Wild (LFW) image set. In addition, we seek to compare the performance of similarity learning methods to descriptor based methods. We present the following results: (1) Descriptor-Based approaches that efficiently encode the appearance of each face image as a vector outperform the leading similarity based method in the task of multi-option face identification. (2) Straightforward use of Euclidean distance on the descriptor vectors performs somewhat worse than the similarity learning methods on the task of pair matching. (3) Adding a learning stage, the performance of descriptor based methods matches and exceeds that of similarity methods on the pair matching task. (4) A novel patch based descriptor we propose is able to improve the performance of the successful Local Binary Pattern (LBP) descriptor in both multi-option identification and same/not-same classification.
MATLAB Code for the Three-Patch LBP (TPLBP) and Four-Patch LBP (FPLBP) global image descriptors
Below please find MATLAB code for producing the TPLBP and FPLBP codes and global image descriptors. Note that this code was not the one used in the experiments reported in the paper, nor are the default parametrs necessarily the same as the ones we used. Please report any bugs or problems to email@example.com.
Type “help TPLBP” or “help FPLBP” for more information on each of these functions.
A typical usage would look something like this:
>> I = rgb2gray(I);
>> [descI, codeI]=FPLBP(I);
>> descI = descI(:);
descI will then contain the FPLBP global image descriptor.
Copyright and disclaimer
Copyright 2008, Lior Wolf and Tal Hassner