Similarity
Metric Learning and Applications to Unconstrained Face Recognition
Face
recognition has attracted increasing attentions due to its applications in
biometrics and surveillance. Recently, considerable research efforts are
devoted to the unconstrained face verification problem, the task of which is to
predict whether two face images represent the same person or not. The face
images are taken under unconstrained conditions and show significant variations
in complex background, lighting, pose, and expression (see e.g. Figure 1). In
addition, the evaluation procedure for face verification typically assumes that
the person identities in the training and test sets are exclusive, requiring
the prediction of never-seen-before faces. Both factors make face verification
very challenging, see e.g. Labeled Faces in the Wild (LFW) dataset.
Figure 1: Example images from the LFW
database exhibit large intra-personal variations: each column is a pair of
images from the same person.
Similarity
metric learning provides a natural solution for face verification by comparing
image pairs based on the similarity or distance metric learnt from the
data. The basic intuition is
that the similarity score between an image-pair from the distinct identity
should be smaller than that between an image-pair from the same identity. The critical challenges of this project
are the following:
a)
How
to extract visual features or template to represent face images?
b)
How
to reduce noise and the large intra-personal variations?
c)
How
to derive a visual similarity metric to effectively compare the similarity
between face images?
d)
How
to design elegant and efficient optimization algorithms for big face-image
data?
Recently, we introduced in [1] a
novel regularization framework of learning a similarity metric suitable for
unconstrained face verification. We formulate its learning objective by
incorporating the robustness to large intra-personal variations and the
discrimination power of novel similarity metrics, a property most existing metric
learning methods do not hold. Our formulation is a convex optimization problem
which guarantees the existence of its global solution.
Table 1: Verification
accuracy comparison on the restricted setting of LFW: our method [1] is denoted
by Sub-SML.
Figure 2:
ROC curve comparison on the restricted setting of LFW: our method [1] is
Sub-SML.
Our method
Sub-SML proposed in [1] has achieved 89.73% on the restricted setting of the
benchmark LFW database which is currently the best result reported so far, see
Table 1 and Figure 2 for more comparison results.
Publications:
1.
Y.Ying, Q. Cao and P. Li, Similarity
metric learning for face recognition. IEEE
International Conference on Computer Vision (ICCV), 2013. (First version, November 2012)
2.
Y. Ying and P.
Li, Distance
metric learning with eigenvalue optimization. Journal of Machine Learning
Research, 2012.
3.
Y. Ying, K. Huang
and C. Campbell, Sparse metric
learning via smooth optimization, Advances in Neural Information
Processing Systems (NIPS), 2009.
4.
K. Huang, Y. Ying
and C. Campbell, GSML: A
unified framework for sparse metric learning, IEEE International
Conference on Data Mining (ICDM), 2009.
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