Abstract: The
One-Shot Similarity measure has recently been introduced as a means of
boosting the performance of face recognition systems. Given two
vectors, their One-Shot Similarity score reflects the likelihood of
each vector belonging to the same class as the other vector and not in
a class defined by a fixed set of "negative" examples. An appealing
aspect of this approach is that it does not require class labeled
training data. In this paper we explore how the One-Shot Similarity may
nevertheless benefit from the availability of such labels. We make the
following contributions: (a) we present a system utilizing subject and
pose information to improve facial image pair-matching performance
using multiple One-Shot scores; (b) we show how separating pose and
identity may lead to better face recognition rates in unconstrained,
"wild" facial images; (c) we explore how far we can get using a single
descriptor with different similarity tests as opposed to the popular
multiple descriptor approaches; and (d) we demonstrate the benefit of
learned metrics for improved One-Shot performance. We test the
performance of our system on the challenging Labeled Faces in the Wild
unrestricted benchmark and present results that exceed by a large
margin results reported on the restricted benchmark.
Reference:
Yaniv Taigman, Lior
Wolf, and Tal Hassner, "Multiple One-Shots for Utilizing Class Label Information," The British Machine Vision Conference (BMVC),
Sept. 2009.
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Click here for our aligned version of the LFW image set (New!)
Related publications:
The One-Shot Similarity Kernal
Descriptor Based Methods in the Wild
Decoupling pose and identity with multiple One-Shot Similarity (OSS) scores:
Each group contains two images and 10 sample multiple OSS scores.
Identity based multiple OSS scores are plotted with circle markers and
pose based are with squares. As can be seen the value of each type of
OSS score is a good indication of the type of similarity between the
images of the pair. (a) Same person, same pose. (b) Different persons
and pose. (c) Same person, different pose. (d) Different persons, same
pose. (e) Same person and pose, however, a mode of variability not
modeled in the system is present.
Results on the LFW benchmark: ROC
curves averaged over 10 folds of View 2 of the LFW data set. Each point
on the curve represents the average over the 10 folds of (false
positive rate, true positive rate) for a fixed threshold. The proposed
method (single and multiple descriptors) is compared to the best
algorithms as reported on the LFW results page.
These algorithms include the combined Nowak+MERL [14], the Nowak
method [20], the hybrid method of [27] and as well using our alignment
technique (Section 4.1), the V1-like/MKL methodof [24] and the recent
LMDL/MkNN methods of [11]. (u) indicates ROC curve is for the
unrestricted setting.