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Descriptor Based Methods in the Wild
Lior Wolf1
Tal Hassner2
Yaniv Taigman1,3
1. The School of Computer Science,
Tel-Aviv University, Israel
2. Computer Science Division
The Open University of Israel
3. face.com,
Tel-Aviv, Israel
Abstract: Recent methods for learning similarity between images have presented
impressive results in the problem of pair matching (same/not-same 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.
Reference:
Lior Wolf, Tal Hassner and Yaniv Taigman,
"Descriptor Based Methods in the Wild," Faces in Real-Life Images workshop
at the European Conference on Computer Vision (ECCV), Oct 2008.
Click here for the
PDF (1,098kb)
Click here for
the BibTex
Best result on
the
LFW face recognition benchmark,
at the Faces in Real-Life Images workshop,
2008
Related publications:
Multiple One-Shots for Utilizing Class Label Information
The One-Shot Similarity Kernal
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 talhassner@gmail.com.
Type "help TPLBP" or "help FPLBP" for more information on each of these functions.
A typical usage would look something like this:
>> I = imread(...);
>> I = rgb2gray(I);
>> [descI, codeI]=FPLBP(I);
>> descI = descI(:);
descI will then contain the FPLBP global image descriptor.
Downloads:
TPLBP.m
FPLBP.m
Copyright and disclaimer:
Copyright 2008, Lior Wolf and Tal Hassner
Copyright: no material is allowed to be
copied or used in any way without written permission of the authors.
Last update 22th of July, 2009
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