In recent years there has been an increasing interest in several fields of research for analyzing human behavior. Typically these works focus on macro-level recognition such as activities or explicit human-human interactions. Systems built to address these tasks assume that what is recognized corresponds to the true intentions of the observed person. This may not be always the case, since macro-level behaviors can be easily counterfeited. For this reason, the interest in understanding micro-level behaviors has emerged, since these are often connected to involuntary reactions which are less likely to be faked or concealed.
Understanding strong, unbiased behaviors is also a key aspect in several fields. For example, more accurate interest profiles could be built from facial micro-expressions, leading to better product or content suggestions. In security critical applications, observing posture, gait and small body movements could reveal malicious intentions. Moreover, body movements and behaviors could also be used to profile or re-identificate subjects without harming their privacy.
In general, low-level expressions and behaviors are more difficult to recognize than high-level ones, due to the fine-grained nature of the problem. It is also a fact that with the recent technological advancement, the means of data acquisition and processing have also dramatically improved, enabling new applications and analyses. To reliably understand facial micro-expressions, for example, there are both spatial and temporal issues to take into account. On the one hand, it is necessary to either acquire and process high-resolution images or rely on different kinds of data such as high-quality depth maps. On the other hand, micro-expressions occur over an extremely short timespan (<500ms) and might not even be detectable with conventional low-framerate cameras. If some years ago it was not possible to acquire this kind of data and process them, now it certainly is.
In this workshop we are interested in submissions focused but not limited on low-level characteristics, either facial or related to the human body, aiming at understanding high-level concepts. We are also interested in applications leveraging this information, for user profiling or behavior understanding. The workshop also favors positive criticism of current works and encourages new perspectives on the matter. Moreover, as we are all living in a world where our personal data is used and often abused, we are also interested in privacy issues that arise when profiling users. Finally, we also promote the creation of new datasets for both facial micro-expressions and body behavior understanding.
The workshop is an online event.
Submissions are invited from all areas of pattern recognition. Topics of interest include (but are not limited to):
Face expressions
Emotion recognition
Behavior analysis
Action recognition
Gait recognition
3D recognition
Micro-expression recognition
Body analysis
Biometrics
User profiling and suggestion
Deep learning tailored for face and behavior recognition
Representation learning
Unsupervised and semi-supervised learning
Multi-resolution, multi-sensor, multi-modal analysis
Public benchmark datasets and evaluation protocols
We invite authors to submit unpublished papers (15 pages including references ICPR format), to be presented at an oral/poster session upon acceptance. All submissions will go through a single-blind review process. All contributions must be submitted on CMT: https://cmt3.research.microsoft.com/FBE2024
The accepted papers will be published in Lecture Notes in Computer Science (LNCS), Springer (https://www.springer.com/gp/computer-science/lncs) in the ICPR workshop proceedings.