Gang Hua, IBM Research T. J. Watson Center
Ming-Hsuan Yang, University of California, Merced
Erik Learned-Miller, University of Massachusetts, Amherst
Yi Ma, Microsoft Research Asia
Matthew Turk, University of California, Santa Barbara
David Kriegman, University of California, San Diego
Thomas Huang, University of Illinois at Urbana-Champaign
Research on face recognition has had a revival in recent years. This is largely due to the need for robust face recognition technologies for consumers to tag digital photos and facilitate their organization and online sharing. Unlike traditional access control scenarios, where facial images are taken under controlled lighting, pose, and expression, face recognition in consumer digital imaging suffers from uncontrolled lighting, large pose variation, a range of facial expressions, make-up, and severe partial occlusions. Likewise, in many surveillance scenarios, video may be acquired in uncontrolled situations, from moving cameras. These factors have challenged robust face recognition for decades, but they are still unresolved.
In a traditional face recognition system, a set of labeled gallery faces is first compiled; a new probe image is then matched against the gallery face database to be recognized as a known face or rejected. Depending how much control we have on the gallery and probe faces, we can roughly categorize face recognition tasks into three scenarios: face recognition under well-controlled, moderately controlled, or uncontrolled environments. In a well controlled environment, image capture is constrained for both the gallery and probe faces. In a moderately controlled environment, we lose control of either the gallery faces or the probe faces, but not both. In an uncontrolled environment, we have control over neither. We can similarly define these three categories for face recognition tasks such as face clustering.
Face recognition in well-controlled environments is relatively mature and has been heavily studied, but face recognition in uncontrolled or moderately controlled environments is still in its early stages. While earlier face recognition methods applied pattern recognition and machine learning techniques for matching in the image space and subsequent work focused on geometric, lighting and reflectance models of faces, these techniques alone appear insufficient to solve new set of challenges. However, many are under the impression that face recognition in general is solved, or that the uncontrolled scenarios are too difficult to solve in practice. Neither seems to be the case. Practical face tagging systems are emerging based on existing face recognition technologies. For these, a good user interface (UI) and user experience (UX) design is essential in order to compensate for the possible failures from the face recognition algorithm. Moreover, in many of these applications, there may be additional contextual information we can leverage to improve face recognition accuracy. Multidisciplinary approaches may be necessary to deliver real working systems.
This PAMI special issue is dedicated to addressing the interesting challenges in this domain and to promote systematic research and evaluation of promising methods and systems. We encourage the submission of novel solutions and thorough evaluations of issues in face recognition in environments that are not highly controlled, including, but not necessarily limited to:
Design of robust face similarity features and metrics
Robust face clustering and sorting algorithms
Novel user interaction models and face recognition algorithms for face tagging
Novel applications of face recognition on the web
Novel computational paradigms for face recognition
Challenges in large scale face recognition tasks, e.g., on the internet
Face recognition with contextual information
Face recognition benchmarks and evaluation methodology for moderately controlled or uncontrolled environments
Video face recognition
Extended to 04/15/2010
First review results: 05/28/2010
Revision due: 06/25/2010
Second review results: 07/23/2010
Final manuscript due: 08/20/2010
Publication date: 01/2011
Papers should be well aligned with the theme of the special issue and must be submitted online through the PAMI manuscript central site, with a note/tag designating the manuscript for this special issue. All submissions will be peer-reviewed by at least three experts in the field. Priority will be given to work with high novelty and potential impact. Particular attention will be given to comparisons with the state of the art, and to the discussion of statistical significance of the results.