International Workshop on Faces in Real-Life Images: Detection, Alignment,
and Recognition (In conjunction with ECCV 2008)

Call for Papers

Electronic submission due: July 23, 2008.
Decisions: August 15, 2008.
Camera ready due: August 25, 2008

There is rapidly increasing interest in the field of face recognition in
image and videos; while there has been a great deal of progress in the
past 10 years, much of the work is restricted to constrained settings in
which one or more of the many variables that affect appearance, such as
lighting, pose, or facial expression, has been controlled. We believe that
focusing specifically on ?real-life? data sets will foster the development
of new and more general techniques, and will ultimately result in more
flexible face recognition systems. This is a new and interesting emergent
direction, and we will make this workshop the right place for discussion
and for the sharing of ideas on this topic.

One aim of the workshop is to encourage collaboration between researchers
who do face detection and recognition but may not be familiar with more
general object recognition techniques, and those who do object recognition
work but have not considered the application of their methods to the face
recognition problem. It is interesting to note that one of the best known
algorithms for unconstrained face recognition using random forests does
not appear on the ?Face Recognition Web Page?. This is probably because
the algorithm is known as a generic object recognition algorithm. We want
to bring together people from the ?object recognition? community and the
?face recognition? community, and try to understand if they are distinct
only for historical reasons or if they rely on different foundations.

We solicit contributions in two categories.

Category A: Novel methods in Detection, Alignment, and Recognition

Papers in this category should present novel scientific contributions in
the detection, alignment, or recognition of faces. We are particularly
interested in the domain of unconstrained faces in which faces are not
presented in a laboratory controlled setting. We encourage authors to show
their results on the LFW database, although this is not essential for
publication. We are also interested in relationships among detection,
alignment and recognition. For example, how can recognition algorithms be
used to improve detection performance? Or how do various alignment
algorithms effect standard recognition algorithms? We are also interested
in the use of hidden variable models, random field models, and other
probabilistic models for solving any of these problems. Methods that
incorporate an unsupervised, semi-supervised, or transfer learning method
are also solicited.

Category B: Unconstrained Face Recognition Challenge.
The goal of these submissions is to compare algorithms for the
unconstrained face recognition problem, and should present results on the
Labeled Faces in the Wild database . Authors may submit either a short
paper or a regular paper in this Category.

For short papers (two pages or less), the authors need only include face
recognition results, as described below. These results will be summarized
and described by the organizers during the workshop. Authors may give a
short description of their methods or refer to other publications which
give the details of the algorithms used. Short papers will not appear as
separate publications in the workshop proceedings, but will be described
collectively in a single summary article describing results on the
challenge.

For regular papers (of standard ECCV format and length), authors should
fully describe algorithms so that the code can be recreated by others. If
accepted, these papers will be included in the proceedings of the
workshop, but the authors may or may not be allocated an oral
presentation, depending upon time availability. Papers that are submitted
both to the main ECCV conference and to the workshop will be considered.

Details of the LFW database, including formats of data, and organization
of training, validation, and testing components, are described in the LFW
technical report. It is not essential that an algorithm achieve
state-of-the-art performance in order to be published at the workshop,
although performance of the algorithm will be an important criterion in
establishing the quality of the work. Papers that do not achieve
state-of-the-art results may be published in the workshop proceedings
depending upon the novelty of the proposed methods, but may not be
allocated a talk, since the workshop time is limited. Such contributions
will be summarized in a presentation on the overall Unconstrained Face
Recognition Challenge.

Performance Reporting. Every paper in Category B should report the
estimated mean accuracy and the standard error of the mean, as defined on
page 7 of the LFW technical report. Users should report results for
image-restricted training (described in Section IV.A. of the technical
report), but may also report results for unrestricted training (Section
IV.B.) if desired.

We also encourage authors to submit with their papers data files of
results allow the creation of Precision-Recall curves. Instructions about
the format of these files will be posted shortly.


Format of papers

For both Categories of papers, please use the standard ECCV template for
paper submissions, including the short papers. Submissions should NOT be
anonymous. Papers longer than 14 pages will be returned without review.
There is no minimum paper length.

website: https://www.cs.umass.edu/~elm/realfaces